{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XJMRPP7RI4I4XC3L77EHGGQGX4","short_pith_number":"pith:XJMRPP7R","schema_version":"1.0","canonical_sha256":"ba5917bff14711cb8b6bffc8731a06bf04b5d885398f9ae20dea734336babaf7","source":{"kind":"arxiv","id":"1807.02247","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Learning for Fine-grained Image Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Yang, Kevin Lin, Qiaosong Wang, Robinson Piramuthu","submitted_at":"2018-07-06T04:03:11Z","abstract_excerpt":"Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. In practice, a dynamic inventory with new fine-grained categories adds another dimension to this challenge. In this work, we propose an end-to-end network, called FGGAN, that learns discriminative representations by implicitly learning a geometric transformation from multi-view images for fine-grained image search. We integrate a generative adversarial network (GAN) that can automatically handle complex view and po"},"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":"1807.02247","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-06T04:03:11Z","cross_cats_sorted":[],"title_canon_sha256":"72bc4019fdb46d278ee44541223cdc30749051d38e70df5494ccf5e50242a699","abstract_canon_sha256":"7f3e9a6b90c1d89bd80534751faf9591644ee3938cbe1dca2d4b290c21fa4281"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:22.709441Z","signature_b64":"TuHrrCbuiv3BjhNMlouJEGj//3ZL3I2zL9xDpVgoF2sbeS4W1/hHIhRSZmXEnl7/5F+/Mfn+i8HKwUxOvc0oAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba5917bff14711cb8b6bffc8731a06bf04b5d885398f9ae20dea734336babaf7","last_reissued_at":"2026-05-18T00:11:22.708766Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:22.708766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Learning for Fine-grained Image Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Yang, Kevin Lin, Qiaosong Wang, Robinson Piramuthu","submitted_at":"2018-07-06T04:03:11Z","abstract_excerpt":"Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. In practice, a dynamic inventory with new fine-grained categories adds another dimension to this challenge. In this work, we propose an end-to-end network, called FGGAN, that learns discriminative representations by implicitly learning a geometric transformation from multi-view images for fine-grained image search. We integrate a generative adversarial network (GAN) that can automatically handle complex view and po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02247","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":""},"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":"1807.02247","created_at":"2026-05-18T00:11:22.708869+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02247v1","created_at":"2026-05-18T00:11:22.708869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02247","created_at":"2026-05-18T00:11:22.708869+00:00"},{"alias_kind":"pith_short_12","alias_value":"XJMRPP7RI4I4","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XJMRPP7RI4I4XC3L","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XJMRPP7R","created_at":"2026-05-18T12:33:01.666342+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/XJMRPP7RI4I4XC3L77EHGGQGX4","json":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4.json","graph_json":"https://pith.science/api/pith-number/XJMRPP7RI4I4XC3L77EHGGQGX4/graph.json","events_json":"https://pith.science/api/pith-number/XJMRPP7RI4I4XC3L77EHGGQGX4/events.json","paper":"https://pith.science/paper/XJMRPP7R"},"agent_actions":{"view_html":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4","download_json":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4.json","view_paper":"https://pith.science/paper/XJMRPP7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02247&json=true","fetch_graph":"https://pith.science/api/pith-number/XJMRPP7RI4I4XC3L77EHGGQGX4/graph.json","fetch_events":"https://pith.science/api/pith-number/XJMRPP7RI4I4XC3L77EHGGQGX4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4/action/storage_attestation","attest_author":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4/action/author_attestation","sign_citation":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4/action/citation_signature","submit_replication":"https://pith.science/pith/XJMRPP7RI4I4XC3L77EHGGQGX4/action/replication_record"}},"created_at":"2026-05-18T00:11:22.708869+00:00","updated_at":"2026-05-18T00:11:22.708869+00:00"}