{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:AJL4NL4JJ65TBJOCLLYKL6RMOL","short_pith_number":"pith:AJL4NL4J","canonical_record":{"source":{"id":"1812.08468","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-20T10:32:46Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"28cbcc3ae3947848c6310612b3fb893d105a7467f6d2330ec52249cfcc9324bb","abstract_canon_sha256":"7581c9ddfc32de1ff6abdc00e70943b849149a707f6f4bdd4620706b9fe686d7"},"schema_version":"1.0"},"canonical_sha256":"0257c6af894fbb30a5c25af0a5fa2c72c6714e810f5bcef90da55c8dad103693","source":{"kind":"arxiv","id":"1812.08468","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.08468","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"arxiv_version","alias_value":"1812.08468v5","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08468","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_12","alias_value":"AJL4NL4JJ65T","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_16","alias_value":"AJL4NL4JJ65TBJOC","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_8","alias_value":"AJL4NL4J","created_at":"2026-07-05T00:20:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:AJL4NL4JJ65TBJOCLLYKL6RMOL","target":"record","payload":{"canonical_record":{"source":{"id":"1812.08468","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-20T10:32:46Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"28cbcc3ae3947848c6310612b3fb893d105a7467f6d2330ec52249cfcc9324bb","abstract_canon_sha256":"7581c9ddfc32de1ff6abdc00e70943b849149a707f6f4bdd4620706b9fe686d7"},"schema_version":"1.0"},"canonical_sha256":"0257c6af894fbb30a5c25af0a5fa2c72c6714e810f5bcef90da55c8dad103693","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:42.635372Z","signature_b64":"IWvWn+NaVzdT576JYiTfvlsjst7fn/NvXX5kfD/MRlQvfoTWtirh2eCCvI/YXG2yF+TD1BZ04tWIUs24BXSdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0257c6af894fbb30a5c25af0a5fa2c72c6714e810f5bcef90da55c8dad103693","last_reissued_at":"2026-07-05T00:20:42.634933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:42.634933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.08468","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:20:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tEp1A+q9QLtyI/Hbc0vC3edVBMY7dX4puPEZh+81rBSeaNScLxYE99qWFNrPSEpIL8FErqdF0B/qwIxAmfeFCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:05:56.906333Z"},"content_sha256":"d08b2434755c01deb4ba0c48f3366bff14e408fb45a8361c4f8106fac7ecd573","schema_version":"1.0","event_id":"sha256:d08b2434755c01deb4ba0c48f3366bff14e408fb45a8361c4f8106fac7ecd573"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:AJL4NL4JJ65TBJOCLLYKL6RMOL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"One-Class Feature Learning Using Intra-Class Splitting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bin Yang, Patrick Schlachter, Yiwen Liao","submitted_at":"2018-12-20T10:32:46Z","abstract_excerpt":"This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature extractors. In contrast, the proposed method realizes feature learning by splitting the given normal class into typical and atypical normal samples. By introducing closeness loss and dispersion loss, an intra-class joint training procedure between the two subsets after splitting "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08468","kind":"arxiv","version":5},"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/1812.08468/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":"2026-07-05T00:20:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GRsUD5nKzQI1bWF5RbVZdXHAlQkViUcHoDyfHreyC6xNpoGF1aGaHn5rZ1aygjEy2PPEdI3OM2R4qevz5NK8Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T15:05:56.907007Z"},"content_sha256":"ad4e1840f9ebf06b40b39b2a194b9dde743e13df2ee4770129d32c7468a7abf1","schema_version":"1.0","event_id":"sha256:ad4e1840f9ebf06b40b39b2a194b9dde743e13df2ee4770129d32c7468a7abf1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/bundle.json","state_url":"https://pith.science/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/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":"2026-08-17T15:05:56Z","links":{"resolver":"https://pith.science/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL","bundle":"https://pith.science/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/bundle.json","state":"https://pith.science/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJL4NL4JJ65TBJOCLLYKL6RMOL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:AJL4NL4JJ65TBJOCLLYKL6RMOL","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":"7581c9ddfc32de1ff6abdc00e70943b849149a707f6f4bdd4620706b9fe686d7","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-20T10:32:46Z","title_canon_sha256":"28cbcc3ae3947848c6310612b3fb893d105a7467f6d2330ec52249cfcc9324bb"},"schema_version":"1.0","source":{"id":"1812.08468","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.08468","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"arxiv_version","alias_value":"1812.08468v5","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08468","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_12","alias_value":"AJL4NL4JJ65T","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_16","alias_value":"AJL4NL4JJ65TBJOC","created_at":"2026-07-05T00:20:42Z"},{"alias_kind":"pith_short_8","alias_value":"AJL4NL4J","created_at":"2026-07-05T00:20:42Z"}],"graph_snapshots":[{"event_id":"sha256:ad4e1840f9ebf06b40b39b2a194b9dde743e13df2ee4770129d32c7468a7abf1","target":"graph","created_at":"2026-07-05T00:20:42Z","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/1812.08468/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature extractors. In contrast, the proposed method realizes feature learning by splitting the given normal class into typical and atypical normal samples. By introducing closeness loss and dispersion loss, an intra-class joint training procedure between the two subsets after splitting ","authors_text":"Bin Yang, Patrick Schlachter, Yiwen Liao","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-20T10:32:46Z","title":"One-Class Feature Learning Using Intra-Class Splitting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08468","kind":"arxiv","version":5},"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:d08b2434755c01deb4ba0c48f3366bff14e408fb45a8361c4f8106fac7ecd573","target":"record","created_at":"2026-07-05T00:20:42Z","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":"7581c9ddfc32de1ff6abdc00e70943b849149a707f6f4bdd4620706b9fe686d7","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-20T10:32:46Z","title_canon_sha256":"28cbcc3ae3947848c6310612b3fb893d105a7467f6d2330ec52249cfcc9324bb"},"schema_version":"1.0","source":{"id":"1812.08468","kind":"arxiv","version":5}},"canonical_sha256":"0257c6af894fbb30a5c25af0a5fa2c72c6714e810f5bcef90da55c8dad103693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0257c6af894fbb30a5c25af0a5fa2c72c6714e810f5bcef90da55c8dad103693","first_computed_at":"2026-07-05T00:20:42.634933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:20:42.634933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IWvWn+NaVzdT576JYiTfvlsjst7fn/NvXX5kfD/MRlQvfoTWtirh2eCCvI/YXG2yF+TD1BZ04tWIUs24BXSdAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:20:42.635372Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.08468","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d08b2434755c01deb4ba0c48f3366bff14e408fb45a8361c4f8106fac7ecd573","sha256:ad4e1840f9ebf06b40b39b2a194b9dde743e13df2ee4770129d32c7468a7abf1"],"state_sha256":"248f2471631d10411efe3ec4ae68bfbc00b0d85facf1e482e31a751328c21ff6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PKZ7qibQ7+co3bqzhimyMtPdrLrQCr4nw4yrPUV/c0PUSwO67bpOHWtcZTb699wPcknom1teANZS68SflwP7Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T15:05:56.911527Z","bundle_sha256":"bbc4232b16fa98ddc2296d228fa69af03e3adb2f30d17b90d76c010327a1835b"}}