{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SKELFVSAHZOQZI5JSGESTX3POQ","short_pith_number":"pith:SKELFVSA","schema_version":"1.0","canonical_sha256":"9288b2d6403e5d0ca3a9918929df6f743ab13da2617a9d326a4c2e83fc03e993","source":{"kind":"arxiv","id":"1907.03644","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kirthi Shankar Sivamani","submitted_at":"2019-07-08T14:22:54Z","abstract_excerpt":"Dataset bias is a well-known problem in the field of computer vision. The presence of implicit bias in any image collection hinders a model trained and validated on a particular dataset to yield similar accuracies when tested on other datasets. In this paper, we propose a novel debiasing technique to reduce the effects of a biased training dataset. Our goal is to augment the training data using a generative network by learning a non-linear mapping from the source domain (training set) to the target domain (testing set) while retaining training set labels. The cycle consistency loss and adversa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1907.03644","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-08T14:22:54Z","cross_cats_sorted":[],"title_canon_sha256":"9a36d14e824ab4482ecfa0a910f95d505cfeade2c2b917e7b2304eac496b12b9","abstract_canon_sha256":"1a217655642baa0893126e58f5aaa560458e385cde24584e242a1ec511046129"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:48.581082Z","signature_b64":"bsV4EpUwjN8+P/W5By/DEHs0S362wRp37ZLuEy2mttZUmGp49eTf94SK796/TQ2QqVmc3sXOuAKB6Hq26gnIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9288b2d6403e5d0ca3a9918929df6f743ab13da2617a9d326a4c2e83fc03e993","last_reissued_at":"2026-05-17T23:40:48.580352Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:48.580352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Domain Alignment to Mitigate Low Level Dataset Biases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kirthi Shankar Sivamani","submitted_at":"2019-07-08T14:22:54Z","abstract_excerpt":"Dataset bias is a well-known problem in the field of computer vision. The presence of implicit bias in any image collection hinders a model trained and validated on a particular dataset to yield similar accuracies when tested on other datasets. In this paper, we propose a novel debiasing technique to reduce the effects of a biased training dataset. Our goal is to augment the training data using a generative network by learning a non-linear mapping from the source domain (training set) to the target domain (testing set) while retaining training set labels. The cycle consistency loss and adversa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03644","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.03644","created_at":"2026-05-17T23:40:48.580471+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03644v2","created_at":"2026-05-17T23:40:48.580471+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03644","created_at":"2026-05-17T23:40:48.580471+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKELFVSAHZOQ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKELFVSAHZOQZI5J","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKELFVSA","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ","json":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ.json","graph_json":"https://pith.science/api/pith-number/SKELFVSAHZOQZI5JSGESTX3POQ/graph.json","events_json":"https://pith.science/api/pith-number/SKELFVSAHZOQZI5JSGESTX3POQ/events.json","paper":"https://pith.science/paper/SKELFVSA"},"agent_actions":{"view_html":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ","download_json":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ.json","view_paper":"https://pith.science/paper/SKELFVSA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03644&json=true","fetch_graph":"https://pith.science/api/pith-number/SKELFVSAHZOQZI5JSGESTX3POQ/graph.json","fetch_events":"https://pith.science/api/pith-number/SKELFVSAHZOQZI5JSGESTX3POQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ/action/storage_attestation","attest_author":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ/action/author_attestation","sign_citation":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ/action/citation_signature","submit_replication":"https://pith.science/pith/SKELFVSAHZOQZI5JSGESTX3POQ/action/replication_record"}},"created_at":"2026-05-17T23:40:48.580471+00:00","updated_at":"2026-05-17T23:40:48.580471+00:00"}