{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:CSUTGYMODOYM3J3EEUQZH6OBUH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"31356bc5269606b014b3fa967084787e2b052cc238ed59c4543a403c54556809","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-12T02:57:28Z","title_canon_sha256":"b68dbb1738dc7fb0b581964b73cde865e5271d6637b31f9beab59a1554be8626"},"schema_version":"1.0","source":{"id":"2004.05529","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.05529","created_at":"2026-07-05T00:54:38Z"},{"alias_kind":"arxiv_version","alias_value":"2004.05529v1","created_at":"2026-07-05T00:54:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.05529","created_at":"2026-07-05T00:54:38Z"},{"alias_kind":"pith_short_12","alias_value":"CSUTGYMODOYM","created_at":"2026-07-05T00:54:38Z"},{"alias_kind":"pith_short_16","alias_value":"CSUTGYMODOYM3J3E","created_at":"2026-07-05T00:54:38Z"},{"alias_kind":"pith_short_8","alias_value":"CSUTGYMO","created_at":"2026-07-05T00:54:38Z"}],"graph_snapshots":[{"event_id":"sha256:ffea8642f8df8cde12a48423298b8db4de6ba00a47580993907d8f740fa88837","target":"graph","created_at":"2026-07-05T00:54:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2004.05529/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient and activation of the pre-trained network. We show that our model provides a local linear approximation to an underlying deep model, and discuss important theoretical insights. Moreover, we present an e","authors_text":"Fangzhou Mu, Yingyu Liang, Yin Li","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-12T02:57:28Z","title":"Gradients as Features for Deep Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.05529","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b3c5db072da223f42b3b3d11df8b64aaddddbb791df40d1ca5df8fb33b23d3ab","target":"record","created_at":"2026-07-05T00:54:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"31356bc5269606b014b3fa967084787e2b052cc238ed59c4543a403c54556809","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-12T02:57:28Z","title_canon_sha256":"b68dbb1738dc7fb0b581964b73cde865e5271d6637b31f9beab59a1554be8626"},"schema_version":"1.0","source":{"id":"2004.05529","kind":"arxiv","version":1}},"canonical_sha256":"14a933618e1bb0cda764252193f9c1a1ea8b5ee5703c2cb4d933cd7de611242f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14a933618e1bb0cda764252193f9c1a1ea8b5ee5703c2cb4d933cd7de611242f","first_computed_at":"2026-07-05T00:54:38.070343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:54:38.070343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WzJkcs+pTey6y4ThUhyI/pMxt2Z8hhz9KiXkMUHibjM9aJFfL9PGmkw5AHicZKfJ+HjPfxY6O7ntp5GG4RaVDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:54:38.070785Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.05529","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b3c5db072da223f42b3b3d11df8b64aaddddbb791df40d1ca5df8fb33b23d3ab","sha256:ffea8642f8df8cde12a48423298b8db4de6ba00a47580993907d8f740fa88837"],"state_sha256":"2990a8ffe530105a09f5d82da266aedc6a68bf2d3b76af9c60faf8f01eebac86"}