{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4STBG2R6RJA7Y6OW25ZELLYMWC","short_pith_number":"pith:4STBG2R6","canonical_record":{"source":{"id":"2302.00194","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T02:55:26Z","cross_cats_sorted":[],"title_canon_sha256":"9691fbc8720a3aebefcfad09aa14faef49d84797031f2910a61885b8119726df","abstract_canon_sha256":"f9c49e3b58a9503f407ee6947abe412cbb6aeb3f6ffbb8bf7fe76002d40b0999"},"schema_version":"1.0"},"canonical_sha256":"e4a6136a3e8a41fc79d6d77245af0cb08de5eb6f0d34eacf6cc40a8e730c59fb","source":{"kind":"arxiv","id":"2302.00194","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00194","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00194v1","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00194","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_12","alias_value":"4STBG2R6RJA7","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_16","alias_value":"4STBG2R6RJA7Y6OW","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_8","alias_value":"4STBG2R6","created_at":"2026-07-05T05:38:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4STBG2R6RJA7Y6OW25ZELLYMWC","target":"record","payload":{"canonical_record":{"source":{"id":"2302.00194","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T02:55:26Z","cross_cats_sorted":[],"title_canon_sha256":"9691fbc8720a3aebefcfad09aa14faef49d84797031f2910a61885b8119726df","abstract_canon_sha256":"f9c49e3b58a9503f407ee6947abe412cbb6aeb3f6ffbb8bf7fe76002d40b0999"},"schema_version":"1.0"},"canonical_sha256":"e4a6136a3e8a41fc79d6d77245af0cb08de5eb6f0d34eacf6cc40a8e730c59fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:38:00.675956Z","signature_b64":"GlnzF2haq+e+XREQxg+ndxRg3a2U4Ir0+nUIJ0P2kUTbrxRfPgnp3G5CH70jf3XNBi9qHDGiviN5hKgLqjALBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4a6136a3e8a41fc79d6d77245af0cb08de5eb6f0d34eacf6cc40a8e730c59fb","last_reissued_at":"2026-07-05T05:38:00.675527Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:38:00.675527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.00194","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":"2026-07-05T05:38:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jt+5agHyaAHfekQLMxhuBG0dfONdPY14KVc3Hmx17Ponq2Kem/8fWBMX7+XkP5NJj+Lv2+8VRZmAPamNdCIXCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:14:54.384943Z"},"content_sha256":"d3b649d52c18843f874d049e4952040fc359e3fc214e93ed441772c83dc4679b","schema_version":"1.0","event_id":"sha256:d3b649d52c18843f874d049e4952040fc359e3fc214e93ed441772c83dc4679b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4STBG2R6RJA7Y6OW25ZELLYMWC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Free Lunch for Domain Adversarial Training: Environment Label Smoothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jian Liang, Liang Wang, Rong Jin, Tieniu Tan, Xue Wang, Yifan Zhang, Zhang Zhang","submitted_at":"2023-02-01T02:55:26Z","abstract_excerpt":"A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the imp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00194","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/2302.00194/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":"2026-07-05T05:38:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"58t5JO8dgR/YalqA9dRsKVuuUDsCj8t0wyRT3MZQ9jBWygnHZufnSUenGVZe1isCBd7qrgVpqx4six9mViYUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:14:54.385441Z"},"content_sha256":"927c4392e491f6eb478eeba8eeb49e3d550db549c711ad9959365e137888934b","schema_version":"1.0","event_id":"sha256:927c4392e491f6eb478eeba8eeb49e3d550db549c711ad9959365e137888934b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/bundle.json","state_url":"https://pith.science/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/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":"2026-08-04T15:14:54Z","links":{"resolver":"https://pith.science/pith/4STBG2R6RJA7Y6OW25ZELLYMWC","bundle":"https://pith.science/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/bundle.json","state":"https://pith.science/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4STBG2R6RJA7Y6OW25ZELLYMWC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4STBG2R6RJA7Y6OW25ZELLYMWC","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":"f9c49e3b58a9503f407ee6947abe412cbb6aeb3f6ffbb8bf7fe76002d40b0999","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T02:55:26Z","title_canon_sha256":"9691fbc8720a3aebefcfad09aa14faef49d84797031f2910a61885b8119726df"},"schema_version":"1.0","source":{"id":"2302.00194","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.00194","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"arxiv_version","alias_value":"2302.00194v1","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00194","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_12","alias_value":"4STBG2R6RJA7","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_16","alias_value":"4STBG2R6RJA7Y6OW","created_at":"2026-07-05T05:38:00Z"},{"alias_kind":"pith_short_8","alias_value":"4STBG2R6","created_at":"2026-07-05T05:38:00Z"}],"graph_snapshots":[{"event_id":"sha256:927c4392e491f6eb478eeba8eeb49e3d550db549c711ad9959365e137888934b","target":"graph","created_at":"2026-07-05T05:38:00Z","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/2302.00194/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the imp","authors_text":"Jian Liang, Liang Wang, Rong Jin, Tieniu Tan, Xue Wang, Yifan Zhang, Zhang Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T02:55:26Z","title":"Free Lunch for Domain Adversarial Training: Environment Label Smoothing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00194","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:d3b649d52c18843f874d049e4952040fc359e3fc214e93ed441772c83dc4679b","target":"record","created_at":"2026-07-05T05:38:00Z","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":"f9c49e3b58a9503f407ee6947abe412cbb6aeb3f6ffbb8bf7fe76002d40b0999","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-01T02:55:26Z","title_canon_sha256":"9691fbc8720a3aebefcfad09aa14faef49d84797031f2910a61885b8119726df"},"schema_version":"1.0","source":{"id":"2302.00194","kind":"arxiv","version":1}},"canonical_sha256":"e4a6136a3e8a41fc79d6d77245af0cb08de5eb6f0d34eacf6cc40a8e730c59fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4a6136a3e8a41fc79d6d77245af0cb08de5eb6f0d34eacf6cc40a8e730c59fb","first_computed_at":"2026-07-05T05:38:00.675527Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:38:00.675527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GlnzF2haq+e+XREQxg+ndxRg3a2U4Ir0+nUIJ0P2kUTbrxRfPgnp3G5CH70jf3XNBi9qHDGiviN5hKgLqjALBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:38:00.675956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.00194","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3b649d52c18843f874d049e4952040fc359e3fc214e93ed441772c83dc4679b","sha256:927c4392e491f6eb478eeba8eeb49e3d550db549c711ad9959365e137888934b"],"state_sha256":"853aa2f1f94e109de33932fb5615ce31221a50557f7ccc5ac5bb6510175ede1e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VWyT4sviZPmB0B6faUASWR8C7/CPWP5jsWHL87sc7f2Xefp9RVIrFmpaw2y6YYD3Tu1+H9E34mx5T4cGdlg3DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:14:54.389409Z","bundle_sha256":"60463bde8e37a97a09d2e615ac1ee1da7a59e6fdbaa827f728d063b7a664b9c8"}}