{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:6YFQK6OJA3FRPOEU5UMC3HFRGI","short_pith_number":"pith:6YFQK6OJ","canonical_record":{"source":{"id":"2012.06908","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-12T21:53:55Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"1633c691350e091de39bc29311cdd8b761546be0e3381e431590c550896440e9","abstract_canon_sha256":"b75a5fc7999143229b1ad9afd75af002e996c076530aec16aa2403e6c8d713fc"},"schema_version":"1.0"},"canonical_sha256":"f60b0579c906cb17b894ed182d9cb1322afc7cd4bca1233b8170f2971ce750ca","source":{"kind":"arxiv","id":"2012.06908","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.06908","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"arxiv_version","alias_value":"2012.06908v2","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.06908","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_12","alias_value":"6YFQK6OJA3FR","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YFQK6OJA3FRPOEU","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YFQK6OJ","created_at":"2026-07-05T02:27:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:6YFQK6OJA3FRPOEU5UMC3HFRGI","target":"record","payload":{"canonical_record":{"source":{"id":"2012.06908","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-12T21:53:55Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"1633c691350e091de39bc29311cdd8b761546be0e3381e431590c550896440e9","abstract_canon_sha256":"b75a5fc7999143229b1ad9afd75af002e996c076530aec16aa2403e6c8d713fc"},"schema_version":"1.0"},"canonical_sha256":"f60b0579c906cb17b894ed182d9cb1322afc7cd4bca1233b8170f2971ce750ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:11.062630Z","signature_b64":"1k45fEDN8WO7kYGF1GYrLWf46ljKH+DNc+m6x8mZRdyhapEQcu1iNpZPVhLBhPGtQHqqEqwDZTLpbKc7kV63DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f60b0579c906cb17b894ed182d9cb1322afc7cd4bca1233b8170f2971ce750ca","last_reissued_at":"2026-07-05T02:27:11.062151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:11.062151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.06908","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:27:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8laVTmvVb46hay+oIW8uMDoQlxeX10cMHgo5hGjeXho9CXnoivobfVq4mpJfXZTB5YiBxi8uKpeiSpfmluv3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:50:33.321583Z"},"content_sha256":"59b85291e5886aaa34251bacd58db0ba311358079ccfb5678bff5bf8b09533b0","schema_version":"1.0","event_id":"sha256:59b85291e5886aaa34251bacd58db0ba311358079ccfb5678bff5bf8b09533b0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:6YFQK6OJA3FRPOEU5UMC3HFRGI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Jonathan Frankle, Michael Carbin, Shiyu Chang, Sijia Liu, Tianlong Chen, Yang Zhang, Zhangyang Wang","submitted_at":"2020-12-12T21:53:55Z","abstract_excerpt":"The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies suggest that pre-training benefits from gigantic model capacity. We are hereby curious and ask: after pre-training, does a pre-trained model indeed have to stay large for its downstream transferability?\n  In this paper, we examine supervised an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.06908","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2012.06908/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:27:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fDV3MZa0hdmbySw3K/Lo/Q0R66GmQNqvj2HwjoKU0SSIcFFVRLCgb9V3ONXPZd0+r+9GDXv99j1AUSfF613EAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:50:33.321957Z"},"content_sha256":"8e9300381b1412efd10481860b0e072bee3d1bf51dda4e27e6f44478302a4119","schema_version":"1.0","event_id":"sha256:8e9300381b1412efd10481860b0e072bee3d1bf51dda4e27e6f44478302a4119"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/bundle.json","state_url":"https://pith.science/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T13:50:33Z","links":{"resolver":"https://pith.science/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI","bundle":"https://pith.science/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/bundle.json","state":"https://pith.science/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YFQK6OJA3FRPOEU5UMC3HFRGI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6YFQK6OJA3FRPOEU5UMC3HFRGI","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":"b75a5fc7999143229b1ad9afd75af002e996c076530aec16aa2403e6c8d713fc","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-12T21:53:55Z","title_canon_sha256":"1633c691350e091de39bc29311cdd8b761546be0e3381e431590c550896440e9"},"schema_version":"1.0","source":{"id":"2012.06908","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.06908","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"arxiv_version","alias_value":"2012.06908v2","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.06908","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_12","alias_value":"6YFQK6OJA3FR","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YFQK6OJA3FRPOEU","created_at":"2026-07-05T02:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YFQK6OJ","created_at":"2026-07-05T02:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:8e9300381b1412efd10481860b0e072bee3d1bf51dda4e27e6f44478302a4119","target":"graph","created_at":"2026-07-05T02:27:11Z","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/2012.06908/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies suggest that pre-training benefits from gigantic model capacity. We are hereby curious and ask: after pre-training, does a pre-trained model indeed have to stay large for its downstream transferability?\n  In this paper, we examine supervised an","authors_text":"Jonathan Frankle, Michael Carbin, Shiyu Chang, Sijia Liu, Tianlong Chen, Yang Zhang, Zhangyang Wang","cross_cats":["cs.CV","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-12T21:53:55Z","title":"The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.06908","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":"sha256:59b85291e5886aaa34251bacd58db0ba311358079ccfb5678bff5bf8b09533b0","target":"record","created_at":"2026-07-05T02:27:11Z","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":"b75a5fc7999143229b1ad9afd75af002e996c076530aec16aa2403e6c8d713fc","cross_cats_sorted":["cs.CV","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-12-12T21:53:55Z","title_canon_sha256":"1633c691350e091de39bc29311cdd8b761546be0e3381e431590c550896440e9"},"schema_version":"1.0","source":{"id":"2012.06908","kind":"arxiv","version":2}},"canonical_sha256":"f60b0579c906cb17b894ed182d9cb1322afc7cd4bca1233b8170f2971ce750ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f60b0579c906cb17b894ed182d9cb1322afc7cd4bca1233b8170f2971ce750ca","first_computed_at":"2026-07-05T02:27:11.062151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:27:11.062151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1k45fEDN8WO7kYGF1GYrLWf46ljKH+DNc+m6x8mZRdyhapEQcu1iNpZPVhLBhPGtQHqqEqwDZTLpbKc7kV63DA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:27:11.062630Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.06908","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:59b85291e5886aaa34251bacd58db0ba311358079ccfb5678bff5bf8b09533b0","sha256:8e9300381b1412efd10481860b0e072bee3d1bf51dda4e27e6f44478302a4119"],"state_sha256":"30f093aa156253f3847473ec40d65f04c28de2376fa48ff598d451714edc9290"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G/PP0x/LzrA9iEb+D39RBleO0meY0G66e7ZqLO6fRzhXL2cSCM9N/gh64ijKswM+VxOhP9SOsRtbPtSEnMX0CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:50:33.323896Z","bundle_sha256":"90e46d3f968edccad2bdd06e06878ebbb4852046640b1a076a57550be0f3ac33"}}