{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:KSAE6GYL57OGEV2VKTXCWTS747","short_pith_number":"pith:KSAE6GYL","canonical_record":{"source":{"id":"2012.13493","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-25T02:44:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4e9de7595856fe3c36a6e8df29656e04f9fdf96c3e0ecfec8f2c5269e1be2ec0","abstract_canon_sha256":"5524a5ffdc7e69e73f46c288a2d106afd56b97f8b556dfef5f27a8f5f8b45fb5"},"schema_version":"1.0"},"canonical_sha256":"54804f1b0befdc62575554ee2b4e5fe7f0e791e1e8b4101e7bd59b9041858ed2","source":{"kind":"arxiv","id":"2012.13493","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.13493","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"arxiv_version","alias_value":"2012.13493v2","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.13493","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_12","alias_value":"KSAE6GYL57OG","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_16","alias_value":"KSAE6GYL57OGEV2V","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_8","alias_value":"KSAE6GYL","created_at":"2026-07-05T02:04:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:KSAE6GYL57OGEV2VKTXCWTS747","target":"record","payload":{"canonical_record":{"source":{"id":"2012.13493","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-25T02:44:22Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4e9de7595856fe3c36a6e8df29656e04f9fdf96c3e0ecfec8f2c5269e1be2ec0","abstract_canon_sha256":"5524a5ffdc7e69e73f46c288a2d106afd56b97f8b556dfef5f27a8f5f8b45fb5"},"schema_version":"1.0"},"canonical_sha256":"54804f1b0befdc62575554ee2b4e5fe7f0e791e1e8b4101e7bd59b9041858ed2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:14.404911Z","signature_b64":"/nJQgEWAETJ96Nc46rHeCWGakYGRJUWjlUde4YjQjxOJQs4tQuK9TsesGnRDKgRbxChbR6CbnjeO3izyMcnQAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54804f1b0befdc62575554ee2b4e5fe7f0e791e1e8b4101e7bd59b9041858ed2","last_reissued_at":"2026-07-05T02:04:14.404559Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:14.404559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.13493","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":"2026-07-05T02:04:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GwWOlh066y+SzwKTCRKXUSQHUdpAqOc2gtDW1cqJPMRHsHFc9LmNBgpfirLxbV/84VzgB3t3sT4fwdA2/+WFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T00:30:46.072852Z"},"content_sha256":"3544f7a2843fd35456b52e00ca2b35bb4129b9b42af1867b9585afe13c9dc469","schema_version":"1.0","event_id":"sha256:3544f7a2843fd35456b52e00ca2b35bb4129b9b42af1867b9585afe13c9dc469"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:KSAE6GYL57OGEV2VKTXCWTS747","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-supervised Pre-training with Hard Examples Improves Visual Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Baolin Peng, Chunyuan Li, Jianfeng Gao, Lei Zhang, Mingyuan Zhou, Xiujun Li","submitted_at":"2020-12-25T02:44:22Z","abstract_excerpt":"Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling framework that unifies existing SSP methods as learning to predict pseudo-labels. Then, we propose new data augmentation methods of generating training examples whose pseudo-labels are harder to predict than those generated via random image transformations. Specifically, we use adversarial training and CutMix to create hard examples (HEXA) to be used as augmented views for MoCo-v2 and DeepCluster-v2, leading to two vari"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.13493","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/2012.13493/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":"2026-07-05T02:04:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"88QB+FuS5616FoVWno/HIe9hcy9jqoDRpHsVXKZbSrWZb5WolCpHkblvlTapNsOOfT+XjRv/ha5ySmo2J7O0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-16T00:30:46.073239Z"},"content_sha256":"6806d8df0ac3348794c98308c177c201a1e8956dad10cf4e6d8450fdf7864785","schema_version":"1.0","event_id":"sha256:6806d8df0ac3348794c98308c177c201a1e8956dad10cf4e6d8450fdf7864785"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KSAE6GYL57OGEV2VKTXCWTS747/bundle.json","state_url":"https://pith.science/pith/KSAE6GYL57OGEV2VKTXCWTS747/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KSAE6GYL57OGEV2VKTXCWTS747/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":"2026-07-16T00:30:46Z","links":{"resolver":"https://pith.science/pith/KSAE6GYL57OGEV2VKTXCWTS747","bundle":"https://pith.science/pith/KSAE6GYL57OGEV2VKTXCWTS747/bundle.json","state":"https://pith.science/pith/KSAE6GYL57OGEV2VKTXCWTS747/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KSAE6GYL57OGEV2VKTXCWTS747/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KSAE6GYL57OGEV2VKTXCWTS747","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":"5524a5ffdc7e69e73f46c288a2d106afd56b97f8b556dfef5f27a8f5f8b45fb5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-25T02:44:22Z","title_canon_sha256":"4e9de7595856fe3c36a6e8df29656e04f9fdf96c3e0ecfec8f2c5269e1be2ec0"},"schema_version":"1.0","source":{"id":"2012.13493","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.13493","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"arxiv_version","alias_value":"2012.13493v2","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.13493","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_12","alias_value":"KSAE6GYL57OG","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_16","alias_value":"KSAE6GYL57OGEV2V","created_at":"2026-07-05T02:04:14Z"},{"alias_kind":"pith_short_8","alias_value":"KSAE6GYL","created_at":"2026-07-05T02:04:14Z"}],"graph_snapshots":[{"event_id":"sha256:6806d8df0ac3348794c98308c177c201a1e8956dad10cf4e6d8450fdf7864785","target":"graph","created_at":"2026-07-05T02:04:14Z","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/2012.13493/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling framework that unifies existing SSP methods as learning to predict pseudo-labels. Then, we propose new data augmentation methods of generating training examples whose pseudo-labels are harder to predict than those generated via random image transformations. Specifically, we use adversarial training and CutMix to create hard examples (HEXA) to be used as augmented views for MoCo-v2 and DeepCluster-v2, leading to two vari","authors_text":"Baolin Peng, Chunyuan Li, Jianfeng Gao, Lei Zhang, Mingyuan Zhou, Xiujun Li","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-25T02:44:22Z","title":"Self-supervised Pre-training with Hard Examples Improves Visual Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.13493","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":"sha256:3544f7a2843fd35456b52e00ca2b35bb4129b9b42af1867b9585afe13c9dc469","target":"record","created_at":"2026-07-05T02:04:14Z","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":"5524a5ffdc7e69e73f46c288a2d106afd56b97f8b556dfef5f27a8f5f8b45fb5","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-12-25T02:44:22Z","title_canon_sha256":"4e9de7595856fe3c36a6e8df29656e04f9fdf96c3e0ecfec8f2c5269e1be2ec0"},"schema_version":"1.0","source":{"id":"2012.13493","kind":"arxiv","version":2}},"canonical_sha256":"54804f1b0befdc62575554ee2b4e5fe7f0e791e1e8b4101e7bd59b9041858ed2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54804f1b0befdc62575554ee2b4e5fe7f0e791e1e8b4101e7bd59b9041858ed2","first_computed_at":"2026-07-05T02:04:14.404559Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:04:14.404559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/nJQgEWAETJ96Nc46rHeCWGakYGRJUWjlUde4YjQjxOJQs4tQuK9TsesGnRDKgRbxChbR6CbnjeO3izyMcnQAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:04:14.404911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.13493","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3544f7a2843fd35456b52e00ca2b35bb4129b9b42af1867b9585afe13c9dc469","sha256:6806d8df0ac3348794c98308c177c201a1e8956dad10cf4e6d8450fdf7864785"],"state_sha256":"4bf5da14bd45964a2c8f2ca1255ab7a992e21abb7963f18abfa381470a67bf07"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ou+A5DvmHhWnP42Usv1uxC0vzAkTbDs//i3wYmHsogtxPoAxrjVGJygk//Xh9nniKiYVPl7OvWXzWl+xLCXICQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-16T00:30:46.075499Z","bundle_sha256":"2f9573ad77d157ac96288de22f5905d26f2c709f2a7bd599a7d1d8cfa5592b1a"}}