{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:PE3WINAN722MUNT2YBBZDFBEJP","short_pith_number":"pith:PE3WINAN","canonical_record":{"source":{"id":"2109.08052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T15:35:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cbe34f559bf21fc4ead587f2127c00d17994fda2c1be68430db1da69b69eefb8","abstract_canon_sha256":"1ebd0a89d32221264822eb564645686e1c599c44deeeec1feb3510a205eeb99a"},"schema_version":"1.0"},"canonical_sha256":"793764340dfeb4ca367ac0439194244bff2cefb4e894604f1a2fe89dd4b5602d","source":{"kind":"arxiv","id":"2109.08052","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08052","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08052v1","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08052","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_12","alias_value":"PE3WINAN722M","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_16","alias_value":"PE3WINAN722MUNT2","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_8","alias_value":"PE3WINAN","created_at":"2026-07-05T03:15:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:PE3WINAN722MUNT2YBBZDFBEJP","target":"record","payload":{"canonical_record":{"source":{"id":"2109.08052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T15:35:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cbe34f559bf21fc4ead587f2127c00d17994fda2c1be68430db1da69b69eefb8","abstract_canon_sha256":"1ebd0a89d32221264822eb564645686e1c599c44deeeec1feb3510a205eeb99a"},"schema_version":"1.0"},"canonical_sha256":"793764340dfeb4ca367ac0439194244bff2cefb4e894604f1a2fe89dd4b5602d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:04.657517Z","signature_b64":"AqxbLvicj7CvShOBHRvSZGNVRwzW5VNa2X35jG8iBrcOkRaI1yDPT8uWDys0JxEV+9T0R8HTemTyZspotTzwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"793764340dfeb4ca367ac0439194244bff2cefb4e894604f1a2fe89dd4b5602d","last_reissued_at":"2026-07-05T03:15:04.657124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:04.657124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.08052","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":"2026-07-05T03:15:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q00hR0v8Y2sgWiPNmPv6IUhoFttZyXH0rHdQfWMxiT26FFuVUzJ/cu/MJS3kxvvFfR7Owd10HQFGS8Jzf+9/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:51:37.286487Z"},"content_sha256":"fb64e055a2128d3b635c825239f22799b3173f0be3ff065e229ad37fe6e2c688","schema_version":"1.0","event_id":"sha256:fb64e055a2128d3b635c825239f22799b3173f0be3ff065e229ad37fe6e2c688"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:PE3WINAN722MUNT2YBBZDFBEJP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Visual Representation Learning for Fashion Compatibility","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ambareesh Revanur, Deepthi Sharma, Vijay Kumar","submitted_at":"2021-09-16T15:35:38Z","abstract_excerpt":"We consider the problem of complementary fashion prediction. Existing approaches focus on learning an embedding space where fashion items from different categories that are visually compatible are closer to each other. However, creating such labeled outfits is intensive and also not feasible to generate all possible outfit combinations, especially with large fashion catalogs. In this work, we propose a semi-supervised learning approach where we leverage large unlabeled fashion corpus to create pseudo-positive and pseudo-negative outfits on the fly during training. For each labeled outfit in a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08052","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/2109.08052/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":"2026-07-05T03:15:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kFOEJFSl1utYA/oY7b/BZKCQIO1/zmY0w1YHVecU5nlRUdUMZdxAA4OLBJ36Ub0BrTah48hZmKgtLxH789z3AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:51:37.287030Z"},"content_sha256":"f10ba297f027475826890af2557de1e3a773551eec939c37d8de51901de3df17","schema_version":"1.0","event_id":"sha256:f10ba297f027475826890af2557de1e3a773551eec939c37d8de51901de3df17"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PE3WINAN722MUNT2YBBZDFBEJP/bundle.json","state_url":"https://pith.science/pith/PE3WINAN722MUNT2YBBZDFBEJP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PE3WINAN722MUNT2YBBZDFBEJP/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":"2026-08-05T20:51:37Z","links":{"resolver":"https://pith.science/pith/PE3WINAN722MUNT2YBBZDFBEJP","bundle":"https://pith.science/pith/PE3WINAN722MUNT2YBBZDFBEJP/bundle.json","state":"https://pith.science/pith/PE3WINAN722MUNT2YBBZDFBEJP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PE3WINAN722MUNT2YBBZDFBEJP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PE3WINAN722MUNT2YBBZDFBEJP","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":"1ebd0a89d32221264822eb564645686e1c599c44deeeec1feb3510a205eeb99a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T15:35:38Z","title_canon_sha256":"cbe34f559bf21fc4ead587f2127c00d17994fda2c1be68430db1da69b69eefb8"},"schema_version":"1.0","source":{"id":"2109.08052","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08052","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08052v1","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08052","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_12","alias_value":"PE3WINAN722M","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_16","alias_value":"PE3WINAN722MUNT2","created_at":"2026-07-05T03:15:04Z"},{"alias_kind":"pith_short_8","alias_value":"PE3WINAN","created_at":"2026-07-05T03:15:04Z"}],"graph_snapshots":[{"event_id":"sha256:f10ba297f027475826890af2557de1e3a773551eec939c37d8de51901de3df17","target":"graph","created_at":"2026-07-05T03:15:04Z","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/2109.08052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the problem of complementary fashion prediction. Existing approaches focus on learning an embedding space where fashion items from different categories that are visually compatible are closer to each other. However, creating such labeled outfits is intensive and also not feasible to generate all possible outfit combinations, especially with large fashion catalogs. In this work, we propose a semi-supervised learning approach where we leverage large unlabeled fashion corpus to create pseudo-positive and pseudo-negative outfits on the fly during training. For each labeled outfit in a ","authors_text":"Ambareesh Revanur, Deepthi Sharma, Vijay Kumar","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T15:35:38Z","title":"Semi-Supervised Visual Representation Learning for Fashion Compatibility"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08052","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:fb64e055a2128d3b635c825239f22799b3173f0be3ff065e229ad37fe6e2c688","target":"record","created_at":"2026-07-05T03:15:04Z","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":"1ebd0a89d32221264822eb564645686e1c599c44deeeec1feb3510a205eeb99a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-16T15:35:38Z","title_canon_sha256":"cbe34f559bf21fc4ead587f2127c00d17994fda2c1be68430db1da69b69eefb8"},"schema_version":"1.0","source":{"id":"2109.08052","kind":"arxiv","version":1}},"canonical_sha256":"793764340dfeb4ca367ac0439194244bff2cefb4e894604f1a2fe89dd4b5602d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"793764340dfeb4ca367ac0439194244bff2cefb4e894604f1a2fe89dd4b5602d","first_computed_at":"2026-07-05T03:15:04.657124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:15:04.657124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AqxbLvicj7CvShOBHRvSZGNVRwzW5VNa2X35jG8iBrcOkRaI1yDPT8uWDys0JxEV+9T0R8HTemTyZspotTzwCA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:15:04.657517Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.08052","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb64e055a2128d3b635c825239f22799b3173f0be3ff065e229ad37fe6e2c688","sha256:f10ba297f027475826890af2557de1e3a773551eec939c37d8de51901de3df17"],"state_sha256":"0fa3fb9b7d522ce74e20d886a06f3bde454f3c57aa5d983e7c0bd1b4044581b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uipKrGInNzz9e5Z0CudzPHdlG3EIEZ5z4WLK3eqWUZj+jrU+EnDHWZaLJTzlLMmfeikdaRX5qYdjiTWZbgUXAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T20:51:37.291711Z","bundle_sha256":"fb57b83b048ffc195dad85b2809199ab1c03c8ab75c74dbbaa648c1ce84ac6f8"}}