{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OF6DKMSPHEGRXZZPQZE6BJHJVE","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":"88525d655f047a98d63272f93c3fc9fde2f8eb2393820b4c5e814ebc7af88229","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T18:05:18Z","title_canon_sha256":"8d8dc5c30cdb2bd1760007ffa66c5130c2188ca32c7ad1ad2190874cbf6abe84"},"schema_version":"1.0","source":{"id":"1811.10559","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10559","created_at":"2026-07-05T00:33:43Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10559v2","created_at":"2026-07-05T00:33:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10559","created_at":"2026-07-05T00:33:43Z"},{"alias_kind":"pith_short_12","alias_value":"OF6DKMSPHEGR","created_at":"2026-07-05T00:33:43Z"},{"alias_kind":"pith_short_16","alias_value":"OF6DKMSPHEGRXZZP","created_at":"2026-07-05T00:33:43Z"},{"alias_kind":"pith_short_8","alias_value":"OF6DKMSP","created_at":"2026-07-05T00:33:43Z"}],"graph_snapshots":[{"event_id":"sha256:c238095b064f69969ee583efab0635cac037c1b7c2718ac982828a1fb58d61b7","target":"graph","created_at":"2026-07-05T00:33:43Z","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/1811.10559/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair na\\\"{i}vely, the model is re-optimized to make the filters in these pairs maximally correlated, so that discarding one of the filters from the pair results in minimal information loss. Moreover, after discarding the filters in each round, we further finetune the model to recover fro","authors_text":"Piyush Rai, Pravendra Singh, Vinay Kumar Verma, Vinay P. Namboodiri","cross_cats":["cs.LG","eess.IV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T18:05:18Z","title":"Leveraging Filter Correlations for Deep Model Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10559","kind":"arxiv","version":2},"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:baf18fa5d213ca261f8dffba1216cd9fc503795b22f9e93e34624fd42ebf0443","target":"record","created_at":"2026-07-05T00:33:43Z","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":"88525d655f047a98d63272f93c3fc9fde2f8eb2393820b4c5e814ebc7af88229","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T18:05:18Z","title_canon_sha256":"8d8dc5c30cdb2bd1760007ffa66c5130c2188ca32c7ad1ad2190874cbf6abe84"},"schema_version":"1.0","source":{"id":"1811.10559","kind":"arxiv","version":2}},"canonical_sha256":"717c35324f390d1be72f8649e0a4e9a923f6454f62e8a6bf33bfa18b52e38cf1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"717c35324f390d1be72f8649e0a4e9a923f6454f62e8a6bf33bfa18b52e38cf1","first_computed_at":"2026-07-05T00:33:43.180573Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:33:43.180573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lfTyz6kY/LbwSL4Zf2RAZWNIGP/6zUAa7zHPCmt4YcJma4MDcUVqdG8/Rn+T7YzRli5SwQ+EQ+/VJbZ9iD68CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:33:43.181047Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.10559","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:baf18fa5d213ca261f8dffba1216cd9fc503795b22f9e93e34624fd42ebf0443","sha256:c238095b064f69969ee583efab0635cac037c1b7c2718ac982828a1fb58d61b7"],"state_sha256":"e4024a6b39c0a1e259919e1eaa2e50b5197e9888488e0998bee83e7ddcac268b"}