{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:AGTV5NKHUXODO44PGW4JO5DTJM","short_pith_number":"pith:AGTV5NKH","schema_version":"1.0","canonical_sha256":"01a75eb547a5dc37738f35b89774734b2e9170730fb8224385b93d93dfc4d85d","source":{"kind":"arxiv","id":"1711.10683","version":4},"attestation_state":"computed","paper":{"title":"Patch Correspondences for Interpreting Pixel-level CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aayush Bansal, Chunhui Liu, Deva Ramanan, Victor Fragoso","submitted_at":"2017-11-29T05:13:32Z","abstract_excerpt":"We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e.g., image synthesis and segmentation). It does so by reconstructing both a CNN's input and output image by copy-pasting corresponding patches from the training set with similar feature embeddings. To do so efficiently, it makes of a patch-match-based algorithm that exploits the fact that the patch representations learned by a CNN for pixel level tasks vary smoothly. Finally, we show that CompNN can b"},"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":"1711.10683","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-29T05:13:32Z","cross_cats_sorted":[],"title_canon_sha256":"dca20d93f9cc9c800de023fe04315e5e23009a8f219423e7bf17218766c892fe","abstract_canon_sha256":"f679515bb3d080824f66c3473c92f704422c3e456b24a7337ba5c7d16f080de7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:41.072177Z","signature_b64":"uUsSta5lehTAL79HjoRkercjSBIfx0/1LGt10S8JP3ZnRsv47ibtZ8VnTbUXDYPunqJgSwsVha9yXS1kueEXBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01a75eb547a5dc37738f35b89774734b2e9170730fb8224385b93d93dfc4d85d","last_reissued_at":"2026-05-18T00:06:41.071802Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:41.071802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Patch Correspondences for Interpreting Pixel-level CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aayush Bansal, Chunhui Liu, Deva Ramanan, Victor Fragoso","submitted_at":"2017-11-29T05:13:32Z","abstract_excerpt":"We present compositional nearest neighbors (CompNN), a simple approach to visually interpreting distributed representations learned by a convolutional neural network (CNN) for pixel-level tasks (e.g., image synthesis and segmentation). It does so by reconstructing both a CNN's input and output image by copy-pasting corresponding patches from the training set with similar feature embeddings. To do so efficiently, it makes of a patch-match-based algorithm that exploits the fact that the patch representations learned by a CNN for pixel level tasks vary smoothly. Finally, we show that CompNN can b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10683","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1711.10683","created_at":"2026-05-18T00:06:41.071871+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.10683v4","created_at":"2026-05-18T00:06:41.071871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10683","created_at":"2026-05-18T00:06:41.071871+00:00"},{"alias_kind":"pith_short_12","alias_value":"AGTV5NKHUXOD","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"AGTV5NKHUXODO44P","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"AGTV5NKH","created_at":"2026-05-18T12:31:05.417338+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/AGTV5NKHUXODO44PGW4JO5DTJM","json":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM.json","graph_json":"https://pith.science/api/pith-number/AGTV5NKHUXODO44PGW4JO5DTJM/graph.json","events_json":"https://pith.science/api/pith-number/AGTV5NKHUXODO44PGW4JO5DTJM/events.json","paper":"https://pith.science/paper/AGTV5NKH"},"agent_actions":{"view_html":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM","download_json":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM.json","view_paper":"https://pith.science/paper/AGTV5NKH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.10683&json=true","fetch_graph":"https://pith.science/api/pith-number/AGTV5NKHUXODO44PGW4JO5DTJM/graph.json","fetch_events":"https://pith.science/api/pith-number/AGTV5NKHUXODO44PGW4JO5DTJM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM/action/storage_attestation","attest_author":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM/action/author_attestation","sign_citation":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM/action/citation_signature","submit_replication":"https://pith.science/pith/AGTV5NKHUXODO44PGW4JO5DTJM/action/replication_record"}},"created_at":"2026-05-18T00:06:41.071871+00:00","updated_at":"2026-05-18T00:06:41.071871+00:00"}