{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:NOZ5JNHZAFASGXM576UNSKT3KG","short_pith_number":"pith:NOZ5JNHZ","canonical_record":{"source":{"id":"2307.05025","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-11T05:58:20Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"8c8ed981c2c04a0c04eab85734532b0913632c814a181911b3690e4c7aeff18c","abstract_canon_sha256":"1f7b385a397fdcb49063bcfff15a6fc42a78f09cd4ddd776bb83474b533d2754"},"schema_version":"1.0"},"canonical_sha256":"6bb3d4b4f90141235d9dffa8d92a7b51b9a39cb78bf886872d59e65026f777e4","source":{"kind":"arxiv","id":"2307.05025","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.05025","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"arxiv_version","alias_value":"2307.05025v1","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05025","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_12","alias_value":"NOZ5JNHZAFAS","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"NOZ5JNHZAFASGXM5","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"NOZ5JNHZ","created_at":"2026-07-05T06:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:NOZ5JNHZAFASGXM576UNSKT3KG","target":"record","payload":{"canonical_record":{"source":{"id":"2307.05025","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-11T05:58:20Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"8c8ed981c2c04a0c04eab85734532b0913632c814a181911b3690e4c7aeff18c","abstract_canon_sha256":"1f7b385a397fdcb49063bcfff15a6fc42a78f09cd4ddd776bb83474b533d2754"},"schema_version":"1.0"},"canonical_sha256":"6bb3d4b4f90141235d9dffa8d92a7b51b9a39cb78bf886872d59e65026f777e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:29:25.049533Z","signature_b64":"4kHR7krvWhNfVhaoyRbgB1Yz+J/5dHETeFtlIVyXEqfiOo6sDuwOl1X9yxwMey3ZoW2DO7NKZHyY6Qlh9esWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bb3d4b4f90141235d9dffa8d92a7b51b9a39cb78bf886872d59e65026f777e4","last_reissued_at":"2026-07-05T06:29:25.049104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:29:25.049104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.05025","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:29:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KhzZhQEuuNnIzS/UsMtqBYXD3mLheQ8tyNbB4OVjuQ+kLrfHh+lvIArDEg9ubR2dP/cvh6vSDfQpjoGLYgtWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:15:43.443961Z"},"content_sha256":"32bb87236d37974eaa2e9ac912e950ac2052c27b8383cd45a282f882ef9d2c41","schema_version":"1.0","event_id":"sha256:32bb87236d37974eaa2e9ac912e950ac2052c27b8383cd45a282f882ef9d2c41"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:NOZ5JNHZAFASGXM576UNSKT3KG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Bo Han, Dadong Wang, Huaxi Huang, Hui Kang, Jun Yu, Sheng Liu, Tongliang Liu","submitted_at":"2023-07-11T05:58:20Z","abstract_excerpt":"In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05025","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.05025/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":"2026-07-05T06:29:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JRUplAa4YomvECgAHn5qRRu6ZxeVB2Hm6rReN/TJbOxWs1FI/leL+a16k8BUJiMWJ1JSd+Va6MEyPCjwROQnAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:15:43.444804Z"},"content_sha256":"bc6905ab821812bf2e11fa85a3e76d181c6a3377f0bbb23f84fb5c111920136e","schema_version":"1.0","event_id":"sha256:bc6905ab821812bf2e11fa85a3e76d181c6a3377f0bbb23f84fb5c111920136e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NOZ5JNHZAFASGXM576UNSKT3KG/bundle.json","state_url":"https://pith.science/pith/NOZ5JNHZAFASGXM576UNSKT3KG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NOZ5JNHZAFASGXM576UNSKT3KG/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":"2026-08-09T08:15:43Z","links":{"resolver":"https://pith.science/pith/NOZ5JNHZAFASGXM576UNSKT3KG","bundle":"https://pith.science/pith/NOZ5JNHZAFASGXM576UNSKT3KG/bundle.json","state":"https://pith.science/pith/NOZ5JNHZAFASGXM576UNSKT3KG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NOZ5JNHZAFASGXM576UNSKT3KG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:NOZ5JNHZAFASGXM576UNSKT3KG","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":"1f7b385a397fdcb49063bcfff15a6fc42a78f09cd4ddd776bb83474b533d2754","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-11T05:58:20Z","title_canon_sha256":"8c8ed981c2c04a0c04eab85734532b0913632c814a181911b3690e4c7aeff18c"},"schema_version":"1.0","source":{"id":"2307.05025","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.05025","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"arxiv_version","alias_value":"2307.05025v1","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05025","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_12","alias_value":"NOZ5JNHZAFAS","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"NOZ5JNHZAFASGXM5","created_at":"2026-07-05T06:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"NOZ5JNHZ","created_at":"2026-07-05T06:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:bc6905ab821812bf2e11fa85a3e76d181c6a3377f0bbb23f84fb5c111920136e","target":"graph","created_at":"2026-07-05T06:29:25Z","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/2307.05025/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination o","authors_text":"Bo Han, Dadong Wang, Huaxi Huang, Hui Kang, Jun Yu, Sheng Liu, Tongliang Liu","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-11T05:58:20Z","title":"Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05025","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":"sha256:32bb87236d37974eaa2e9ac912e950ac2052c27b8383cd45a282f882ef9d2c41","target":"record","created_at":"2026-07-05T06:29:25Z","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":"1f7b385a397fdcb49063bcfff15a6fc42a78f09cd4ddd776bb83474b533d2754","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-11T05:58:20Z","title_canon_sha256":"8c8ed981c2c04a0c04eab85734532b0913632c814a181911b3690e4c7aeff18c"},"schema_version":"1.0","source":{"id":"2307.05025","kind":"arxiv","version":1}},"canonical_sha256":"6bb3d4b4f90141235d9dffa8d92a7b51b9a39cb78bf886872d59e65026f777e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6bb3d4b4f90141235d9dffa8d92a7b51b9a39cb78bf886872d59e65026f777e4","first_computed_at":"2026-07-05T06:29:25.049104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:29:25.049104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4kHR7krvWhNfVhaoyRbgB1Yz+J/5dHETeFtlIVyXEqfiOo6sDuwOl1X9yxwMey3ZoW2DO7NKZHyY6Qlh9esWCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:29:25.049533Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.05025","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32bb87236d37974eaa2e9ac912e950ac2052c27b8383cd45a282f882ef9d2c41","sha256:bc6905ab821812bf2e11fa85a3e76d181c6a3377f0bbb23f84fb5c111920136e"],"state_sha256":"455af78d7e78afe58bd0f626662ce9fd912fd49485deec054536804c85ef7554"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GiSFWN+2PYWodn6a7q/Z5ij5L7JJ1Ucz3+bqOw3c5kiXXfiRTKu6xB2wCIX35QiTxqbF9vBKfeXrT8G8tGJyCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:15:43.450007Z","bundle_sha256":"f159cd91001e1628f9f5073a50b0beb89cbe86a7a16480d270f0cb665265fbe0"}}