{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EYYJFPD5BIX2ISHNZJDN3Q3FSA","short_pith_number":"pith:EYYJFPD5","schema_version":"1.0","canonical_sha256":"263092bc7d0a2fa448edca46ddc3659031562e5dd4796fe43fd4cf08f6c41c20","source":{"kind":"arxiv","id":"1604.08220","version":1},"attestation_state":"computed","paper":{"title":"Diving deeper into mentee networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Baoxin Li, Ragav Venkatesan","submitted_at":"2016-04-27T20:05:45Z","abstract_excerpt":"Modern computer vision is all about the possession of powerful image representations. Deeper and deeper convolutional neural networks have been built using larger and larger datasets and are made publicly available. A large swath of computer vision scientists use these pre-trained networks with varying degrees of successes in various tasks. Even though there is tremendous success in copying these networks, the representational space is not learnt from the target dataset in a traditional manner. One of the reasons for opting to use a pre-trained network over a network learnt from scratch is tha"},"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":"1604.08220","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-27T20:05:45Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"5dd7aa1335cb12f54fd4375065a079d0509e3956cd9d21ac1026d8c7cf05e8a1","abstract_canon_sha256":"626d668d5d395ae22ab2c1d314965140847a74d10de8d63373f8e48b6636790f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:04.565226Z","signature_b64":"eQOB1+71kCS8uq7Z8Q9Ucp9sHZLNd58HhWS2feSGrki5KNSTA0PXG39jalkNF8fytNrZippCRAtAvzos9ZiYBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"263092bc7d0a2fa448edca46ddc3659031562e5dd4796fe43fd4cf08f6c41c20","last_reissued_at":"2026-05-18T01:16:04.564421Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:04.564421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diving deeper into mentee networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Baoxin Li, Ragav Venkatesan","submitted_at":"2016-04-27T20:05:45Z","abstract_excerpt":"Modern computer vision is all about the possession of powerful image representations. Deeper and deeper convolutional neural networks have been built using larger and larger datasets and are made publicly available. A large swath of computer vision scientists use these pre-trained networks with varying degrees of successes in various tasks. Even though there is tremendous success in copying these networks, the representational space is not learnt from the target dataset in a traditional manner. One of the reasons for opting to use a pre-trained network over a network learnt from scratch is tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.08220","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":""},"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":"1604.08220","created_at":"2026-05-18T01:16:04.564563+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.08220v1","created_at":"2026-05-18T01:16:04.564563+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.08220","created_at":"2026-05-18T01:16:04.564563+00:00"},{"alias_kind":"pith_short_12","alias_value":"EYYJFPD5BIX2","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"EYYJFPD5BIX2ISHN","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"EYYJFPD5","created_at":"2026-05-18T12:30:15.759754+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/EYYJFPD5BIX2ISHNZJDN3Q3FSA","json":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA.json","graph_json":"https://pith.science/api/pith-number/EYYJFPD5BIX2ISHNZJDN3Q3FSA/graph.json","events_json":"https://pith.science/api/pith-number/EYYJFPD5BIX2ISHNZJDN3Q3FSA/events.json","paper":"https://pith.science/paper/EYYJFPD5"},"agent_actions":{"view_html":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA","download_json":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA.json","view_paper":"https://pith.science/paper/EYYJFPD5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.08220&json=true","fetch_graph":"https://pith.science/api/pith-number/EYYJFPD5BIX2ISHNZJDN3Q3FSA/graph.json","fetch_events":"https://pith.science/api/pith-number/EYYJFPD5BIX2ISHNZJDN3Q3FSA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA/action/storage_attestation","attest_author":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA/action/author_attestation","sign_citation":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA/action/citation_signature","submit_replication":"https://pith.science/pith/EYYJFPD5BIX2ISHNZJDN3Q3FSA/action/replication_record"}},"created_at":"2026-05-18T01:16:04.564563+00:00","updated_at":"2026-05-18T01:16:04.564563+00:00"}