{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:M4BQXEODBWUFBWPNIM3A7IJANA","short_pith_number":"pith:M4BQXEOD","schema_version":"1.0","canonical_sha256":"67030b91c30da850d9ed43360fa1206827099c5e0d30a195f5addce97244697b","source":{"kind":"arxiv","id":"2308.13680","version":1},"attestation_state":"computed","paper":{"title":"ACC-UNet: A Completely Convolutional UNet model for the 2020s","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daisuke Kihara, Nabil Ibtehaz","submitted_at":"2023-08-25T21:39:43Z","abstract_excerpt":"This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most influential architectures, has been redesigned with transformers. Recently, the efficacy of convolutional models in vision is being reinvestigated by seminal works such as ConvNext, which elevates a ResNet to Swin Transformer level. Deriving inspiration from this, we aim to improve a purely convolutional UNet model so that it can be on par with the transformer-based models, e.g, Swin-Unet or UCTransNet. We exam"},"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":"2308.13680","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-25T21:39:43Z","cross_cats_sorted":[],"title_canon_sha256":"2cb1914ce8db970915dd45be6808cefca3cb7220a8545aa25f13952ac6485bbe","abstract_canon_sha256":"d46acfb50da2ca765747d434bd5519eec1d338a7a2641c8825f1541c5313e699"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:02.780719Z","signature_b64":"Frmp3mDC6PqY52pKv8rkh1B7uRb3EYPdZPP0O3PtSSkNiIsxZrsCmWwnKg3to6Wv/DlM6E+mEE9SNE5bPWL8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67030b91c30da850d9ed43360fa1206827099c5e0d30a195f5addce97244697b","last_reissued_at":"2026-07-05T06:45:02.780263Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:02.780263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ACC-UNet: A Completely Convolutional UNet model for the 2020s","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daisuke Kihara, Nabil Ibtehaz","submitted_at":"2023-08-25T21:39:43Z","abstract_excerpt":"This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the most influential architectures, has been redesigned with transformers. Recently, the efficacy of convolutional models in vision is being reinvestigated by seminal works such as ConvNext, which elevates a ResNet to Swin Transformer level. Deriving inspiration from this, we aim to improve a purely convolutional UNet model so that it can be on par with the transformer-based models, e.g, Swin-Unet or UCTransNet. We exam"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13680","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/2308.13680/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":"2308.13680","created_at":"2026-07-05T06:45:02.780322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13680v1","created_at":"2026-07-05T06:45:02.780322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13680","created_at":"2026-07-05T06:45:02.780322+00:00"},{"alias_kind":"pith_short_12","alias_value":"M4BQXEODBWUF","created_at":"2026-07-05T06:45:02.780322+00:00"},{"alias_kind":"pith_short_16","alias_value":"M4BQXEODBWUFBWPN","created_at":"2026-07-05T06:45:02.780322+00:00"},{"alias_kind":"pith_short_8","alias_value":"M4BQXEOD","created_at":"2026-07-05T06:45:02.780322+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.18611","citing_title":"Flow marching for a generative PDE foundation model","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2602.11229","citing_title":"Latent Generative Solvers for Generalizable Long-Term Physics Simulation","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA","json":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA.json","graph_json":"https://pith.science/api/pith-number/M4BQXEODBWUFBWPNIM3A7IJANA/graph.json","events_json":"https://pith.science/api/pith-number/M4BQXEODBWUFBWPNIM3A7IJANA/events.json","paper":"https://pith.science/paper/M4BQXEOD"},"agent_actions":{"view_html":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA","download_json":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA.json","view_paper":"https://pith.science/paper/M4BQXEOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13680&json=true","fetch_graph":"https://pith.science/api/pith-number/M4BQXEODBWUFBWPNIM3A7IJANA/graph.json","fetch_events":"https://pith.science/api/pith-number/M4BQXEODBWUFBWPNIM3A7IJANA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA/action/storage_attestation","attest_author":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA/action/author_attestation","sign_citation":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA/action/citation_signature","submit_replication":"https://pith.science/pith/M4BQXEODBWUFBWPNIM3A7IJANA/action/replication_record"}},"created_at":"2026-07-05T06:45:02.780322+00:00","updated_at":"2026-07-05T06:45:02.780322+00:00"}