{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RVUDF7BA2WND2IUXQW4LJFIHAN","short_pith_number":"pith:RVUDF7BA","canonical_record":{"source":{"id":"2103.06179","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T16:50:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3d30c59f14a6f315fc06085c82e8341405f8f056b2642ccda117fe463da6dc56","abstract_canon_sha256":"70e62115b54fdc8f2bd9f37bb22d9cea45fbc76fcff45beafe2d8e6cf91a403e"},"schema_version":"1.0"},"canonical_sha256":"8d6832fc20d59a3d229785b8b4950703416ea94dc13b8d91227166101be98367","source":{"kind":"arxiv","id":"2103.06179","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.06179","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"arxiv_version","alias_value":"2103.06179v1","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.06179","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_12","alias_value":"RVUDF7BA2WND","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_16","alias_value":"RVUDF7BA2WND2IUX","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_8","alias_value":"RVUDF7BA","created_at":"2026-07-05T02:22:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RVUDF7BA2WND2IUXQW4LJFIHAN","target":"record","payload":{"canonical_record":{"source":{"id":"2103.06179","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T16:50:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3d30c59f14a6f315fc06085c82e8341405f8f056b2642ccda117fe463da6dc56","abstract_canon_sha256":"70e62115b54fdc8f2bd9f37bb22d9cea45fbc76fcff45beafe2d8e6cf91a403e"},"schema_version":"1.0"},"canonical_sha256":"8d6832fc20d59a3d229785b8b4950703416ea94dc13b8d91227166101be98367","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:22:02.653664Z","signature_b64":"kgAhGkq1Y0IAn1OpFPLJGJ+CflsIxIG6a6Ra4NAh36NycMK0Cue5+DKidyTNbowTGGtkfNqQ+8/AeIcw9yrgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d6832fc20d59a3d229785b8b4950703416ea94dc13b8d91227166101be98367","last_reissued_at":"2026-07-05T02:22:02.653143Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:22:02.653143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.06179","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":"2026-07-05T02:22:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A4hxn5Ufr0c9SGzn6ZW9j7vinU+QLkjIGQGraHaNetC7H6CnselxQzv/dTDHckGKGmRFIeunPgpN1yoKMyuQDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:55:12.050800Z"},"content_sha256":"5b6155bc0ac9a7b6d6fcb382b50d588f31f4a2ed9ebaf302f15d0217e6ea6710","schema_version":"1.0","event_id":"sha256:5b6155bc0ac9a7b6d6fcb382b50d588f31f4a2ed9ebaf302f15d0217e6ea6710"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RVUDF7BA2WND2IUXQW4LJFIHAN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Christian Reimers, Jakob Runge, Joachim Denzler, Paul Bodesheim","submitted_at":"2021-03-10T16:50:42Z","abstract_excerpt":"Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require learning algorithms that prevent the propagation of bias from the dataset into the classifier. We present a novel adversarial debiasing method, which addresses a feature that is spuriously connected to the labels of training images but statistically"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.06179","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/2103.06179/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":"2026-07-05T02:22:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kd2ePrDpWG6N89IlOXK8vt5KSx47knuctrXR891Q7sKJYFitIfIFJStTp+xYtSnF3DEpvDrhbyGY8pNRHHddAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:55:12.051171Z"},"content_sha256":"1e9fb2b70f24a2c1a3a8396e839c5888f16316a5043fdf1777b55fba03652e63","schema_version":"1.0","event_id":"sha256:1e9fb2b70f24a2c1a3a8396e839c5888f16316a5043fdf1777b55fba03652e63"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/bundle.json","state_url":"https://pith.science/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/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":"2026-07-07T11:55:12Z","links":{"resolver":"https://pith.science/pith/RVUDF7BA2WND2IUXQW4LJFIHAN","bundle":"https://pith.science/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/bundle.json","state":"https://pith.science/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RVUDF7BA2WND2IUXQW4LJFIHAN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RVUDF7BA2WND2IUXQW4LJFIHAN","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":"70e62115b54fdc8f2bd9f37bb22d9cea45fbc76fcff45beafe2d8e6cf91a403e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T16:50:42Z","title_canon_sha256":"3d30c59f14a6f315fc06085c82e8341405f8f056b2642ccda117fe463da6dc56"},"schema_version":"1.0","source":{"id":"2103.06179","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.06179","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"arxiv_version","alias_value":"2103.06179v1","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.06179","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_12","alias_value":"RVUDF7BA2WND","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_16","alias_value":"RVUDF7BA2WND2IUX","created_at":"2026-07-05T02:22:02Z"},{"alias_kind":"pith_short_8","alias_value":"RVUDF7BA","created_at":"2026-07-05T02:22:02Z"}],"graph_snapshots":[{"event_id":"sha256:1e9fb2b70f24a2c1a3a8396e839c5888f16316a5043fdf1777b55fba03652e63","target":"graph","created_at":"2026-07-05T02:22:02Z","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/2103.06179/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bias in classifiers is a severe issue of modern deep learning methods, especially for their application in safety- and security-critical areas. Often, the bias of a classifier is a direct consequence of a bias in the training dataset, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require learning algorithms that prevent the propagation of bias from the dataset into the classifier. We present a novel adversarial debiasing method, which addresses a feature that is spuriously connected to the labels of training images but statistically","authors_text":"Christian Reimers, Jakob Runge, Joachim Denzler, Paul Bodesheim","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T16:50:42Z","title":"Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.06179","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":"sha256:5b6155bc0ac9a7b6d6fcb382b50d588f31f4a2ed9ebaf302f15d0217e6ea6710","target":"record","created_at":"2026-07-05T02:22:02Z","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":"70e62115b54fdc8f2bd9f37bb22d9cea45fbc76fcff45beafe2d8e6cf91a403e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T16:50:42Z","title_canon_sha256":"3d30c59f14a6f315fc06085c82e8341405f8f056b2642ccda117fe463da6dc56"},"schema_version":"1.0","source":{"id":"2103.06179","kind":"arxiv","version":1}},"canonical_sha256":"8d6832fc20d59a3d229785b8b4950703416ea94dc13b8d91227166101be98367","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d6832fc20d59a3d229785b8b4950703416ea94dc13b8d91227166101be98367","first_computed_at":"2026-07-05T02:22:02.653143Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:22:02.653143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kgAhGkq1Y0IAn1OpFPLJGJ+CflsIxIG6a6Ra4NAh36NycMK0Cue5+DKidyTNbowTGGtkfNqQ+8/AeIcw9yrgAw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:22:02.653664Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.06179","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b6155bc0ac9a7b6d6fcb382b50d588f31f4a2ed9ebaf302f15d0217e6ea6710","sha256:1e9fb2b70f24a2c1a3a8396e839c5888f16316a5043fdf1777b55fba03652e63"],"state_sha256":"7e45b2ed2748b75b87507c14f5f97fd4fe4743150f40f52b49a2a42a6c36a202"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nh8Po344mJPqXGZW4CngR1RoM1XULgU99Mf1xdRZZVh6o1Dtaoar/tO8ZQlnqTgf+ajlJlkD+ivRU0ece190DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:55:12.053135Z","bundle_sha256":"426ae628954ef8244cdb528d8b8162d106af2fd12bc30f1565096ab95ce0b51e"}}