{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZBO4E4DXOIGGANIM7IBOHTXYVL","short_pith_number":"pith:ZBO4E4DX","schema_version":"1.0","canonical_sha256":"c85dc27077720c60350cfa02e3cef8aad6a157a8d3aacd24243e5837b79dad1c","source":{"kind":"arxiv","id":"2208.09677","version":4},"attestation_state":"computed","paper":{"title":"Net2Brain: A Toolbox to compare artificial vision models with human brain responses","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.CV","authors_text":"Domenic Bersch, Gemma Roig, Kshitij Dwivedi, Martina Vilas, Radoslaw M. Cichy","submitted_at":"2022-08-20T13:10:28Z","abstract_excerpt":"We introduce Net2Brain, a graphical and command-line user interface toolbox for comparing the representational spaces of artificial deep neural networks (DNNs) and human brain recordings. While different toolboxes facilitate only single functionalities or only focus on a small subset of supervised image classification models, Net2Brain allows the extraction of activations of more than 600 DNNs trained to perform a diverse range of vision-related tasks (e.g semantic segmentation, depth estimation, action recognition, etc.), over both image and video datasets. The toolbox computes the representa"},"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":"2208.09677","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T13:10:28Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"ef7fa051b1649821a6a006498031e038e774ca19b253c2e8224f184738369095","abstract_canon_sha256":"82cd936f1ecc61bc06501409d20ed8c29cfa865803786abf9e915c9ce374e65b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:05.536978Z","signature_b64":"odBDaAvTGepv5U+XfZqeAyFfM/spwhXRSKt++BOIiLINSU5PlaYIEV2vnU2dn7/yjecsk+PwzwArYKqwURLDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c85dc27077720c60350cfa02e3cef8aad6a157a8d3aacd24243e5837b79dad1c","last_reissued_at":"2026-07-05T12:05:05.536420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:05.536420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Net2Brain: A Toolbox to compare artificial vision models with human brain responses","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.CV","authors_text":"Domenic Bersch, Gemma Roig, Kshitij Dwivedi, Martina Vilas, Radoslaw M. Cichy","submitted_at":"2022-08-20T13:10:28Z","abstract_excerpt":"We introduce Net2Brain, a graphical and command-line user interface toolbox for comparing the representational spaces of artificial deep neural networks (DNNs) and human brain recordings. While different toolboxes facilitate only single functionalities or only focus on a small subset of supervised image classification models, Net2Brain allows the extraction of activations of more than 600 DNNs trained to perform a diverse range of vision-related tasks (e.g semantic segmentation, depth estimation, action recognition, etc.), over both image and video datasets. The toolbox computes the representa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09677","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2208.09677/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":"2208.09677","created_at":"2026-07-05T12:05:05.536481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09677v4","created_at":"2026-07-05T12:05:05.536481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09677","created_at":"2026-07-05T12:05:05.536481+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZBO4E4DXOIGG","created_at":"2026-07-05T12:05:05.536481+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZBO4E4DXOIGGANIM","created_at":"2026-07-05T12:05:05.536481+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZBO4E4DX","created_at":"2026-07-05T12:05:05.536481+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06963","citing_title":"Learning in Deep Networks under Dale's Constraint","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL","json":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL.json","graph_json":"https://pith.science/api/pith-number/ZBO4E4DXOIGGANIM7IBOHTXYVL/graph.json","events_json":"https://pith.science/api/pith-number/ZBO4E4DXOIGGANIM7IBOHTXYVL/events.json","paper":"https://pith.science/paper/ZBO4E4DX"},"agent_actions":{"view_html":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL","download_json":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL.json","view_paper":"https://pith.science/paper/ZBO4E4DX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09677&json=true","fetch_graph":"https://pith.science/api/pith-number/ZBO4E4DXOIGGANIM7IBOHTXYVL/graph.json","fetch_events":"https://pith.science/api/pith-number/ZBO4E4DXOIGGANIM7IBOHTXYVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL/action/storage_attestation","attest_author":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL/action/author_attestation","sign_citation":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL/action/citation_signature","submit_replication":"https://pith.science/pith/ZBO4E4DXOIGGANIM7IBOHTXYVL/action/replication_record"}},"created_at":"2026-07-05T12:05:05.536481+00:00","updated_at":"2026-07-05T12:05:05.536481+00:00"}