{"as_of":"2026-08-15T17:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0436a6a781554b60a41b8fb3af4aa65bdfde574347bc4a00d236ed88a3de324a","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:38:39.315513Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.11802/citation-record","integrity":"/paper/2412.11802/integrity","json":"/paper/2412.11802/citation-record.json","paper":"/paper/2412.11802"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:41.262052Z","title":"Reducing the dimensionality of data with neural networks,","venue":null,"work_id":"6352b0e3-f0a8-49f6-9f90-5bb3f09a617b","year":2006},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.434416Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:0b23f7e82f6b2afde066b72e5a09ad4e798bb3ab40c58fc97f73ad08a2bfbdf1","observation_id":"8a34dff8-cd4b-45e4-ad39-07fd2fb7221a","resolution":{"observed_at":"2026-08-11T14:38:41.265845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:41.252338Z","title":"Reconstruction by inpainting for visual anomaly detection,","venue":null,"work_id":"2fd573fc-8b93-4166-aeb8-e8b9091cd170","year":null},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.438272Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:b12ee38c050af3deec3db9faacda826cca6c0e26bb3800a22b920a486ebdeaa7","observation_id":"10daabd1-1d3c-4e50-a8a1-54cc21ed98e9","resolution":{"observed_at":"2026-08-11T14:38:41.256300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:41.121958Z","title":"Multi-category decom- position editing network for the accurate visual inspection of texture defects,","venue":null,"work_id":"7c045308-c3ef-4110-aa62-2538f818196f","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.445397Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:e0c4b7fab38f8529a8b7a12a83ca4c26647b546d81dee0accae5df0e4aef818b","observation_id":"5d785d57-d137-4af1-b4fa-114debc618b6","resolution":{"observed_at":"2026-08-11T14:38:41.209090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.936170Z","title":"Defect classification and detection using a multitask deep one-class cnn,","venue":null,"work_id":"55679985-d5b9-4c87-9bb3-c6e789c78870","year":null},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.449633Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:98cd6b41c7511aecfe9be4e2f3a58e69443924e0ed975520d45f5a4ae5460788","observation_id":"a2ee13b7-817b-4c7e-9059-779eff317a6b","resolution":{"observed_at":"2026-08-11T14:38:41.042680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.925813Z","title":"Visual anomaly detection via partition memory bank module and error estimation,","venue":null,"work_id":"887a9c87-e7b4-4048-b125-090cad4e6504","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.453406Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:5846203ea648426b7ff4c0d6040bd3415c4c20fa134c0bdd6a0cfa07c4ca8b2c","observation_id":"de0421e9-0bd6-4770-a08b-d0c8efb8c9c4","resolution":{"observed_at":"2026-08-11T14:38:40.929538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.915924Z","title":"Pga-net: Pyramid feature fusion and global context attention network for automated surface defect detection,","venue":null,"work_id":"509d2015-cdea-41fc-a418-ca3200aed2ca","year":2020},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.457026Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:743355dd5dc4b0f80568d66f9a9576fe7fb355f5b4f957a5374de10566efeb40","observation_id":"9cddb9d0-f2dc-4849-8c57-db6374bb11eb","resolution":{"observed_at":"2026-08-11T14:38:40.919342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.905423Z","title":"A-net: An a-shape lightweight neural network for real-time surface defect segmentation,","venue":null,"work_id":"48f9e56c-99df-4432-86dd-a72a09ab11f6","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.460518Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:6fa483c69c3b90b4dae07a28f5b81b7ae366183703b517f335cec3fc59b34c4a","observation_id":"75289bb6-0645-4417-9a9b-7a0747bb9a3b","resolution":{"observed_at":"2026-08-11T14:38:40.908948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.896070Z","title":"Normal reference attention and defective feature perception network for surface defect detection,","venue":null,"work_id":"1ff6b107-69f3-4b92-a494-31548e33da7b","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.523133Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:8e2eddec17269fab0f66f4bce3c8693b073a6b65f4666db1133d862377f502d0","observation_id":"eb26e5d4-2e80-4d91-975c-34fc3d7a5134","resolution":{"observed_at":"2026-08-11T14:38:40.899566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.886492Z","title":"A feature memory rearrangement network for visual inspection of textured surface defects toward edge intelligent manufacturing,","venue":null,"work_id":"a1c775b4-0539-4872-96c9-13a4c8274a1e","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.630323Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:29e0c53865715ccdf384ed6eb431d9f995c518498ed7f266dafb68ec8a415df1","observation_id":"cbae7bf4-9385-434d-be48-1edfbc52d075","resolution":{"observed_at":"2026-08-11T14:38:40.889557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.875634Z","title":"Unsupervised defect segmentation via forgetting-inputting-based feature fusion and multiple hierarchical feature difference,","venue":null,"work_id":"098fbcc6-859e-4eed-bd07-406e9da42605","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.695656Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:bd16a6c203cd6693a23aca1c59aa5e4d740b2569236440e7a51541a0a7585d9d","observation_id":"f08a793a-0ffa-4a3a-8cb2-8808c942e088","resolution":{"observed_at":"2026-08-11T14:38:40.879742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.696922Z","title":"Self-supervised masking for unsupervised anomaly detection and localization,","venue":null,"work_id":"1959d090-aba6-4239-bdf3-a40572e0b052","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.699717Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:3c950ed6fe3fe03e1d37d2ab11cdc770b43a0cb25477cc469920fd4fecf66802","observation_id":"083b2990-8e58-4b92-b8a1-6b2f307c040f","resolution":{"observed_at":"2026-08-11T14:38:40.823492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.547944Z","title":"Masked swin transformer unet for industrial anomaly detection,","venue":null,"work_id":"30670268-b9fb-4024-a219-a55d057a6e16","year":null},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.703376Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:734235dd7db41ccf4c47840524260637741e7436b964d10f4920be90e65e1449","observation_id":"57f290a5-a2ce-4af3-8b73-1af527be9010","resolution":{"observed_at":"2026-08-11T14:38:40.614786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.537639Z","title":"Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection,","venue":null,"work_id":"81b55987-c308-4514-8c4d-0447499bed28","year":2019},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.706701Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:b8779dfefc686760b1c0e0c51dce1f72ccb6a93b6befd3cd62d4e1df1c110e78","observation_id":"0dd3be5a-ce1c-435a-a8d4-b5deac6e5c6e","resolution":{"observed_at":"2026-08-11T14:38:40.541317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.528175Z","title":"Vt- adl: A vision transformer network for image anomaly detection and localization,","venue":null,"work_id":"c2a7f3cc-c42b-4cc2-911b-90b8c24b40f0","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.710608Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:b6deb42dff5a09f320b2101df30de93cf28618e22d88d2732a2e20de7a7ccae2","observation_id":"b382f9bf-4c94-467d-a81c-e3ac2deb0a48","resolution":{"observed_at":"2026-08-11T14:38:40.531765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.518765Z","title":"Memorizing normality to detect anomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection,","venue":null,"work_id":"c8053989-63ef-4447-8a72-222d7311482b","year":2019},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.714210Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:8de78dd080ab9a65579701df042ca7cbffdec9a2411ad9130c12400a1125870c","observation_id":"306b62ab-6b94-4ca1-8552-7cc553232e40","resolution":{"observed_at":"2026-08-11T14:38:40.522110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.508267Z","title":"Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions,","venue":null,"work_id":"eda24a8c-eaa2-49ef-bb61-eb79aa844cc9","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.717971Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:47c7c3dd932abbc4ee2f88bd3457cfa2a4f4bca6dbb82c038c5141393a1ee2b8","observation_id":"dafdf1e4-bd39-4f89-a7e3-6eb19e1133ce","resolution":{"observed_at":"2026-08-11T14:38:40.511519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.499568Z","title":"Divide- and-assemble: Learning block-wise memory for unsupervised anomaly detection,","venue":null,"work_id":"b1a92175-04d3-418a-aadd-bbae35c4ac57","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.721272Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:f94d7d57c845f1c4aff5e2a051ac48783da0f0a8e30618e3612a3be54647160f","observation_id":"90b0337c-5c5f-412b-82c6-c5f5990bfccb","resolution":{"observed_at":"2026-08-11T14:38:40.502654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.489667Z","title":"An unsupervised-learning-based approach for automated defect inspection on textured surfaces,","venue":null,"work_id":"57f2d41a-081e-4ff2-8312-14194548c7f2","year":2018},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.724480Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:74d4829e195d9f5ad1ea13407bd81303757fc819dd480960c286a8e6545189d7","observation_id":"7b853fa3-c98a-4772-b856-e5c22972bb83","resolution":{"observed_at":"2026-08-11T14:38:40.493401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.479376Z","title":"Multiscale feature-clustering- based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects,","venue":null,"work_id":"b3eeef6a-0cdb-482f-86c7-a10c92f21bc6","year":2019},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.728360Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:7fadc05f35cf042ba94b697ae7001e82b887af70067b8676d0c536df20060ba9","observation_id":"11b3ed27-2fa1-46ff-8631-8037514bdb7b","resolution":{"observed_at":"2026-08-11T14:38:40.483755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.469235Z","title":"The unreasonable effectiveness of deep features as a perceptual metric,","venue":null,"work_id":"68283ea7-3d8b-40e5-b2ad-e343cdc3c871","year":2018},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.731884Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:80c6a5556092d1755356d693c53f7983b7678cc9afbfcc67b001e5debd7c7259","observation_id":"2ac39f30-54c4-491f-9af8-13377ba81616","resolution":{"observed_at":"2026-08-11T14:38:40.472444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.458554Z","title":"Cutpaste: Self-supervised learning for anomaly detection and localization,","venue":null,"work_id":"7a9da34b-c868-4008-912f-fa6a05647b43","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.734958Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:c9af18836bef37432f54140ef029782446f5f456a1ab34cbe171ebd42d6f0110","observation_id":"8d94a81d-8cc3-43cc-a9a2-575599dafb9a","resolution":{"observed_at":"2026-08-11T14:38:40.462652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.447867Z","title":"An anomaly feature-editing- based adversarial network for texture defect visual inspection,","venue":null,"work_id":"f6ac250b-6296-4790-823c-1b11520c8d0c","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.738795Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:d4a7786bba37e57799fbc281fda61e2f418e64fe175fcbf4a3331e4f48d99673","observation_id":"70623d01-4dc7-488b-a77b-74fd2792b55d","resolution":{"observed_at":"2026-08-11T14:38:40.451171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.346760Z","title":"Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,","venue":null,"work_id":"279a4dfa-2489-4268-b136-f05c934f5422","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.742130Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:d28f46e27a4df2781a0e57a86c76cd81fd743f178827b8cd79b798e69581d502","observation_id":"b2455d60-b456-40bf-8463-a24ee808c1e3","resolution":{"observed_at":"2026-08-11T14:38:40.402748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.232580Z","title":"Mldfr: A multilevel features restoration method based on damaged images for anomaly detection and localization,","venue":null,"work_id":"894e039b-4c3d-4905-a50e-1c1e86093a49","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.745760Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:998bcf1771e59ff6fbb3a92d3fa26624f174ca1b8cdf90732789eb7b86b367e3","observation_id":"139e4303-8836-4f6d-8d78-ce956e377872","resolution":{"observed_at":"2026-08-11T14:38:40.281384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00349","last_updated":"2022-11-01T09:45:49Z","snapshot_observed_at":"2026-08-15T01:07:18.425968Z","submitted_at":"2022-11-01T09:45:49Z","title":"Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector","version":1},"cited_work":{"arxiv_id":"2211.00349","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.00349","snapshot_observed_at":"2026-08-11T14:38:39.386984Z","title":"Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector","venue":"cs.CV","work_id":"25e7becf-f28c-4ee4-a49d-ce3d3f504d48","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.749132Z"},"links":{"cited_paper":"/paper/2211.00349","citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:d815c8d652043d18f5f0049299cce7b0846c43d694bea00ce864ee0bd9294983","observation_id":"825581ac-495b-432d-a9e1-706a95b7240e","resolution":{"observed_at":"2026-08-11T14:38:39.393767Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.092397Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"43b2aba9-eacf-43ad-8ce0-ae86d65a40a2","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.752962Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:d9a8d7ce15a82a36a36043df1f7003112a56bdcfe4ce348b9d7e8cc4d9be5877","observation_id":"9b756dd3-15ea-482b-836c-3d741aa5496f","resolution":{"observed_at":"2026-08-11T14:38:40.124719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.082501Z","title":"Inpainting transformer for anomaly detection,","venue":null,"work_id":"662599ec-816b-411c-b471-095a27bbe216","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.756627Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:3231a4497ed829261c962aaa3ca5f443441ecaba723c47d51dda600c758cdc48","observation_id":"81f9e6b0-af6a-43ad-ae4f-58719123133b","resolution":{"observed_at":"2026-08-11T14:38:40.086267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.072839Z","title":"Deep one-class classification,","venue":null,"work_id":"b75f702f-bf95-40ba-9303-1299cd4e9d40","year":2018},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.759761Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:004231d7414fde7499df5fd83f072d541b0be0f13b16f337b00fe095ec1324a0","observation_id":"bcef2b94-2d00-4e95-be4c-bc1f32595643","resolution":{"observed_at":"2026-08-11T14:38:40.076347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.063387Z","title":"Patch svdd: Patch-level svdd for anomaly detection and segmentation,","venue":null,"work_id":"be8ac3ca-d85d-4393-9985-11c45c4ef86a","year":2020},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.763121Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:1bd7c9292930890541a47fc525f09cbaff9e3c1e60481a8be9d093ec754bb37c","observation_id":"810f66fd-22b6-4d9b-aa34-f573374a2319","resolution":{"observed_at":"2026-08-11T14:38:40.066875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.054190Z","title":"Panda: Adapting pretrained features for anomaly detection and segmentation,","venue":null,"work_id":"4a2cbe7e-f2b6-486b-b7c9-8e2247a1eba3","year":2020},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.766569Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:59eeff12bee49341661fae294662e7c113ce06d7ece45ec0de14d2d5ef8787ce","observation_id":"6e284b40-4bc6-45ea-b3b3-d478806a5923","resolution":{"observed_at":"2026-08-11T14:38:40.057897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.044326Z","title":"Towards total recall in industrial anomaly detection,","venue":null,"work_id":"22233df5-c753-4b34-b964-97170d24471c","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.770165Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:ec2415d9060e3881b2d4ee4a2c9a3bf1352954bb385676117e5d7f1f1b4c1bda","observation_id":"123c0dda-41dc-4e02-b345-fc7b17ec1e18","resolution":{"observed_at":"2026-08-11T14:38:40.047895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.034320Z","title":"Industrial image anomaly localization based on gaussian clustering of pre-trained feature,","venue":null,"work_id":"e73ccb5d-1314-47b2-93ee-6f3b254b2366","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.773580Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:b6ce394e082137ba12db9e991a2799850d15d06eafc4074e61a7496dfe1e369e","observation_id":"857ba1c1-c55f-42bd-9274-95d705040ddf","resolution":{"observed_at":"2026-08-11T14:38:40.037825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.023728Z","title":"Anomaly detection via reverse distillation from one-class embedding,","venue":null,"work_id":"6592c6f0-94b0-41f2-b0c9-cd410b62861f","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.777322Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:fe2a83936c17061fff99a9132e8d34f276d64bb6ae7862b3b323488d5f067c7c","observation_id":"d89b48e2-40bf-49a0-8cfb-7af4f5477fc4","resolution":{"observed_at":"2026-08-11T14:38:40.027045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.013085Z","title":"Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping,","venue":null,"work_id":"2e4847a3-3813-48f0-ac0e-274bbc0d0254","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.780780Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:fd390ca0a0dc9d5d21943ce6055c88853791e2fb9f8bd9895aaf349fec8c2559","observation_id":"e21d54d1-d03e-44ff-9a25-376062f3ae5c","resolution":{"observed_at":"2026-08-11T14:38:40.016684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:40.001670Z","title":"Multiresolution knowledge distillation for anomaly detection,","venue":null,"work_id":"5561f79c-bff2-435f-b4bb-dcf079ff1437","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.783909Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:4ad221ed3a6c1eac2d392cc15496fb3474e9b013bec97fe3b86553f6290a1087","observation_id":"cb016e9d-69d2-46a4-b8bb-5caea9481868","resolution":{"observed_at":"2026-08-11T14:38:40.005902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-13T10:21:59.687060Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-11T14:38:38.787272Z","title":"Wide residual networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.787272Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:595150a2c9585718dc85ceaf9db37685778fe3a38ebfe3bab47320490d2d8b3c","observation_id":"3f87492b-bdbd-4e98-b7ca-b8f48391160d","resolution":{"observed_at":"2026-08-11T14:38:38.787272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.841683Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"f597b2eb-c10f-4f90-8930-7b37d600a5f1","year":2014},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.792034Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:ca87f9e3bfd6af56ba6608812cb08cb1b71829d27cd32845d5567b2c96a58976","observation_id":"a528c7b9-6617-4b2b-8612-4120110f46ed","resolution":{"observed_at":"2026-08-11T14:38:39.951040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.687592Z","title":"Masked au- toencoders are scalable vision learners,","venue":null,"work_id":"f33e21c0-67ce-4ddb-98eb-15d851eb0528","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.848998Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:a27ada9d64d0acbb2a46b3d0d02fd55ce261c3627717d1e33f07e10429826316","observation_id":"b82a20fe-6d48-4d83-86fc-fb7d9a34a606","resolution":{"observed_at":"2026-08-11T14:38:39.733592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.677291Z","title":"Anomaly composition and decompo- sition network for accurate visual inspection of texture defects,","venue":null,"work_id":"23023086-854f-474e-a832-67c3fb3e0f81","year":null},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.904344Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:58d713efc2fc5d6b56fd4471fc6fe2d70aac95ed94838edfb5bc4faf37d50e66","observation_id":"a4210bcc-dbf1-422f-8047-a8d8c68b72d5","resolution":{"observed_at":"2026-08-11T14:38:39.680848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.668321Z","title":"A unified model for multi-class anomaly detection,","venue":null,"work_id":"ae6dfd2a-1987-4128-85e8-41b196202d6b","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.951271Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:69adf53e4fbdd0acedfc5e3b13b4e0f41ae5a91b7b5f13e7031bd38b66eccdca","observation_id":"d6bdc7eb-cc31-4974-b913-e9de75ecf17b","resolution":{"observed_at":"2026-08-11T14:38:39.671566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.656990Z","title":"Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow,","venue":null,"work_id":"745c2ab1-4c04-4fc3-a98a-4c64b6342220","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.994420Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:7e64071547de33036ebc9959e577c96ce409c09367a054cb7cdf8d416d550d38","observation_id":"18faa730-ce6d-416c-92d3-0d00afe3d228","resolution":{"observed_at":"2026-08-11T14:38:39.660375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.646810Z","title":"Revisiting reverse distillation for anomaly detection,","venue":null,"work_id":"0cd7fc3c-18f8-44a2-b948-9777cd750392","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.047037Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:413a66e1622a8b877539d95badbf6d2a0f54caf1d74779c53841ae72cd1036f2","observation_id":"9329d6d5-4a22-48ec-b00f-b20b2bda326a","resolution":{"observed_at":"2026-08-11T14:38:39.650601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02011","last_updated":"2019-02-01T16:16:28Z","snapshot_observed_at":"2026-08-14T18:55:21.896237Z","submitted_at":"2018-07-05T14:07:23Z","title":"Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.02011","snapshot_observed_at":"2026-08-11T14:38:39.141313Z","title":"Improv- ing unsupervised defect segmentation by applying structural similarity to autoencoders,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.141313Z"},"links":{"cited_paper":"/paper/1807.02011","citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:1447c353ba1a44c549ffe204eaab86c372c202d697bcb430afad5ed102d2d7db","observation_id":"38eea1c4-743f-4bf4-bc69-2fd7f1dcadab","resolution":{"observed_at":"2026-08-11T14:38:39.141313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.635966Z","title":"Unsupervised anomaly segmentation via deep feature reconstruction,","venue":null,"work_id":"be359397-5c80-43d0-b8d6-03cc2e38a0e1","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.233229Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:8b085aee99d58100f3dac87cecabccc24fd22da10e93111cc6d6c1a018a70e84","observation_id":"2b7753c0-a89a-4783-bfd7-d1d89f04a7ba","resolution":{"observed_at":"2026-08-11T14:38:39.639784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07677","last_updated":"2021-11-16T06:33:39Z","snapshot_observed_at":"2026-08-13T17:34:01.767269Z","submitted_at":"2021-11-15T11:15:02Z","title":"FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07677","snapshot_observed_at":"2026-08-11T14:38:39.281825Z","title":"Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.281825Z"},"links":{"cited_paper":"/paper/2111.07677","citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:c613dd327d9a14ec7310087172df54e83096888d44163530dedc674cba3a2628","observation_id":"05af550d-7fd6-4a7d-9a47-c2603bc5c8a4","resolution":{"observed_at":"2026-08-11T14:38:39.281825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.625177Z","title":"Cfa: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,","venue":null,"work_id":"b37d37a1-d6c3-41f2-902a-51f8bff76177","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.286283Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:3591b9764431d31df76daad560950c10050b9c76da703362eda09986c8a959d9","observation_id":"b3c4893f-c8b6-4768-b7a7-b04996efdf5c","resolution":{"observed_at":"2026-08-11T14:38:39.628978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.615147Z","title":"Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings","venue":null,"work_id":"58c6a361-e424-44e7-bc3e-a62115357bcf","year":2019},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.290213Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:7af603f167e52695f432c212d90e9df6f9c7da33fd93450c58b97c06d93cae61","observation_id":"6b3ca598-f1fc-4a98-b040-8775d965e135","resolution":{"observed_at":"2026-08-11T14:38:39.618736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.604131Z","title":"Padim: A patch distribution modeling framework for anomaly detection and localization,","venue":null,"work_id":"44d76a1b-4d4f-4720-8eb3-a788d12cf52d","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.293997Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:69fac3e834f1f4a8042755955878696d14647b9a1f0b71931e40c29944f27b16","observation_id":"3f2e6e93-5ef0-4e05-b42e-a4182555fdac","resolution":{"observed_at":"2026-08-11T14:38:39.608806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.574927Z","title":"Simplenet: A simple network for image anomaly detection and localization,","venue":null,"work_id":"a21dcbec-eb5f-4509-a60b-3e1053e3148e","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.297913Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:f095d3344425c24e1ee22e9defa65d98c8bd5c6dbc7f6a3ff41881cfe1e98803","observation_id":"fc7d24a1-89ed-4c9e-87b8-1dc74adf153e","resolution":{"observed_at":"2026-08-11T14:38:39.596759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.434738Z","title":"A hierarchical transformation- discriminating generative model for few shot anomaly detection,","venue":null,"work_id":"ba2b65bf-35f3-4cc8-bd16-973f626cc6c1","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.301820Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:2abded14ef73871385feb4b05c1248f96708b0985a5f3c659e081fc72050563b","observation_id":"d9198048-2dc7-4d8c-ae8f-167c6ff86ddb","resolution":{"observed_at":"2026-08-11T14:38:39.486711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.423588Z","title":"Same same but differnet: Semi-supervised defect detection with normalizing flows,","venue":null,"work_id":"204d52ac-ab3b-4863-994f-19631e7f56d4","year":2021},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.305233Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:307591eb318195d7439ccf3fdf39426f4493dcf7b555705ea17cbe1cf3b3c647","observation_id":"cd9f5057-f679-49cd-8e6d-3ecc0ca3b0dd","resolution":{"observed_at":"2026-08-11T14:38:39.427575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.412794Z","title":"Registration based few-shot anomaly detection,","venue":null,"work_id":"adbb3a3e-22b3-47a6-9fc2-12a6d5f7607f","year":2022},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.308395Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:3ed6e5c0efc6a99af5af91486f97a36a5d8829fc2a6dbd44fd883d1c48685360","observation_id":"2f4f582d-0f30-4223-b1fb-26f8943edf46","resolution":{"observed_at":"2026-08-11T14:38:39.416937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14228","last_updated":"2023-10-22T08:20:33Z","snapshot_observed_at":"2026-08-13T05:43:51.959603Z","submitted_at":"2023-10-22T08:20:33Z","title":"Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14228","snapshot_observed_at":"2026-08-11T14:38:39.312050Z","title":"Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.312050Z"},"links":{"cited_paper":"/paper/2310.14228","citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:862f5ab4aff5d10530405b651a36d29667797d9ff43e27ec71b7e257b8f059de","observation_id":"f989690a-150c-4b4d-9b2e-2a6a553dcf9c","resolution":{"observed_at":"2026-08-11T14:38:39.312050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:39.401878Z","title":"Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection,","venue":null,"work_id":"9977dde6-38e5-4f22-b132-53482bf02ce9","year":2023},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:39.315513Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:eea03251bee962e677790aeb3c5af4927c0287cc33982c7d3578bb69d674fe19","observation_id":"e4f797dc-dee8-482b-b770-b8996b5a3975","resolution":{"observed_at":"2026-08-11T14:38:39.405814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:38:41.242931Z","title":null,"venue":null,"work_id":"f460111f-ae15-418a-935a-0866e97522bf","year":null},"citing_paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T14:38:38.441777Z"},"links":{"citing_paper":"/paper/2412.11802"},"observation_digest":"sha256:1ed3a6c96fceb6b1255ad3031cd03bf1407a6f2c58b0ea3e5d6f65799d91d62a","observation_id":"40d0a82e-6407-4cfc-a225-31582e189e29","resolution":{"observed_at":"2026-08-11T14:38:41.246804Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11802","last_updated":"2024-12-16T14:12:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T03:47:25.474030Z","submitted_at":"2024-12-16T14:12:06Z","title":"AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":1,"verified_fuzzy":49},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2412.11802."}