{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:QFI7DHLWVPSDRGXQLAMGVRVOQO","short_pith_number":"pith:QFI7DHLW","schema_version":"1.0","canonical_sha256":"8151f19d76abe4389af058186ac6ae8380ec4d959444fe3e8417e8d99765ec8c","source":{"kind":"arxiv","id":"1507.02379","version":2},"attestation_state":"computed","paper":{"title":"Understanding Intra-Class Knowledge Inside CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antonio Torrabla, Bolei Zhou, Donglai Wei, William Freeman","submitted_at":"2015-07-09T05:20:43Z","abstract_excerpt":"Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers.\n  To invert the intra-class knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models. With it, we show how different \"styles\" of templates for an object class are o"},"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":"1507.02379","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-07-09T05:20:43Z","cross_cats_sorted":[],"title_canon_sha256":"f40a45179a2bf3f2d60f30bb39bf66f2703e7f17d6efee91e126cde1057a6cc3","abstract_canon_sha256":"5614804af6d565d5c520d11a6079c67da8dbc7321a1bd570e641f8f6208b3fbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:33.029564Z","signature_b64":"hcS5IfUbPBy3/y2wnb4ZH/Z2MoUuhCfQ2f9UBdvv9UjtRhsmFo4lD0akF0X9zxbGl2/OVHbV6LceraRdOAK4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8151f19d76abe4389af058186ac6ae8380ec4d959444fe3e8417e8d99765ec8c","last_reissued_at":"2026-05-18T01:36:33.029086Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:33.029086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Intra-Class Knowledge Inside CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antonio Torrabla, Bolei Zhou, Donglai Wei, William Freeman","submitted_at":"2015-07-09T05:20:43Z","abstract_excerpt":"Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers.\n  To invert the intra-class knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models. With it, we show how different \"styles\" of templates for an object class are o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.02379","kind":"arxiv","version":2},"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":"1507.02379","created_at":"2026-05-18T01:36:33.029161+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.02379v2","created_at":"2026-05-18T01:36:33.029161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.02379","created_at":"2026-05-18T01:36:33.029161+00:00"},{"alias_kind":"pith_short_12","alias_value":"QFI7DHLWVPSD","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_16","alias_value":"QFI7DHLWVPSDRGXQ","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_8","alias_value":"QFI7DHLW","created_at":"2026-05-18T12:29:37.295048+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/QFI7DHLWVPSDRGXQLAMGVRVOQO","json":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO.json","graph_json":"https://pith.science/api/pith-number/QFI7DHLWVPSDRGXQLAMGVRVOQO/graph.json","events_json":"https://pith.science/api/pith-number/QFI7DHLWVPSDRGXQLAMGVRVOQO/events.json","paper":"https://pith.science/paper/QFI7DHLW"},"agent_actions":{"view_html":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO","download_json":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO.json","view_paper":"https://pith.science/paper/QFI7DHLW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.02379&json=true","fetch_graph":"https://pith.science/api/pith-number/QFI7DHLWVPSDRGXQLAMGVRVOQO/graph.json","fetch_events":"https://pith.science/api/pith-number/QFI7DHLWVPSDRGXQLAMGVRVOQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO/action/storage_attestation","attest_author":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO/action/author_attestation","sign_citation":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO/action/citation_signature","submit_replication":"https://pith.science/pith/QFI7DHLWVPSDRGXQLAMGVRVOQO/action/replication_record"}},"created_at":"2026-05-18T01:36:33.029161+00:00","updated_at":"2026-05-18T01:36:33.029161+00:00"}