{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LFSZKIODVMHGXWAM56VVY6JKGM","short_pith_number":"pith:LFSZKIOD","schema_version":"1.0","canonical_sha256":"59659521c3ab0e6bd80cefab5c792a333bd936e6a65c09d267b9199154f5a0ab","source":{"kind":"arxiv","id":"2409.03745","version":1},"attestation_state":"computed","paper":{"title":"ArtiFade: Learning to Generate High-quality Subject from Blemished Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kwan-Yee K. Wong, Shaozhe Hao, Shuya Yang, Yukang Cao","submitted_at":"2024-09-05T17:57:59Z","abstract_excerpt":"Subject-driven text-to-image generation has witnessed remarkable advancements in its ability to learn and capture characteristics of a subject using only a limited number of images. However, existing methods commonly rely on high-quality images for training and may struggle to generate reasonable images when the input images are blemished by artifacts. This is primarily attributed to the inadequate capability of current techniques in distinguishing subject-related features from disruptive artifacts. In this paper, we introduce ArtiFade to tackle this issue and successfully generate high-qualit"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.03745","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-05T17:57:59Z","cross_cats_sorted":[],"title_canon_sha256":"6b9441998ad02b49473fe1397e6cc9cb1d09db65071606b1091234c12f379f31","abstract_canon_sha256":"b0de20ef5654dae983d2069c3f394c7a93111c4cc17b25d64d920436f28c523d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:43.901528Z","signature_b64":"87JyDsw9YWsT7RV9M73pr8tS8ddxpY80pBacLPQii3SHcmYGiBg8DD8XHjqci26x1Vx6yT0oWQqb/y59JSeAAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59659521c3ab0e6bd80cefab5c792a333bd936e6a65c09d267b9199154f5a0ab","last_reissued_at":"2026-07-05T09:03:43.901037Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:43.901037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ArtiFade: Learning to Generate High-quality Subject from Blemished Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kwan-Yee K. Wong, Shaozhe Hao, Shuya Yang, Yukang Cao","submitted_at":"2024-09-05T17:57:59Z","abstract_excerpt":"Subject-driven text-to-image generation has witnessed remarkable advancements in its ability to learn and capture characteristics of a subject using only a limited number of images. However, existing methods commonly rely on high-quality images for training and may struggle to generate reasonable images when the input images are blemished by artifacts. This is primarily attributed to the inadequate capability of current techniques in distinguishing subject-related features from disruptive artifacts. In this paper, we introduce ArtiFade to tackle this issue and successfully generate high-qualit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03745","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.03745/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.03745","created_at":"2026-07-05T09:03:43.901096+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03745v1","created_at":"2026-07-05T09:03:43.901096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03745","created_at":"2026-07-05T09:03:43.901096+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFSZKIODVMHG","created_at":"2026-07-05T09:03:43.901096+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFSZKIODVMHGXWAM","created_at":"2026-07-05T09:03:43.901096+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFSZKIOD","created_at":"2026-07-05T09:03:43.901096+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM","json":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM.json","graph_json":"https://pith.science/api/pith-number/LFSZKIODVMHGXWAM56VVY6JKGM/graph.json","events_json":"https://pith.science/api/pith-number/LFSZKIODVMHGXWAM56VVY6JKGM/events.json","paper":"https://pith.science/paper/LFSZKIOD"},"agent_actions":{"view_html":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM","download_json":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM.json","view_paper":"https://pith.science/paper/LFSZKIOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03745&json=true","fetch_graph":"https://pith.science/api/pith-number/LFSZKIODVMHGXWAM56VVY6JKGM/graph.json","fetch_events":"https://pith.science/api/pith-number/LFSZKIODVMHGXWAM56VVY6JKGM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM/action/storage_attestation","attest_author":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM/action/author_attestation","sign_citation":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM/action/citation_signature","submit_replication":"https://pith.science/pith/LFSZKIODVMHGXWAM56VVY6JKGM/action/replication_record"}},"created_at":"2026-07-05T09:03:43.901096+00:00","updated_at":"2026-07-05T09:03:43.901096+00:00"}