{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z3LWI2PZY42HF6ZKNLI7NN4XN6","short_pith_number":"pith:Z3LWI2PZ","canonical_record":{"source":{"id":"2204.11041","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-23T10:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"58aab7315e3ef8812ee1a8cfa053752ec487fbbefd6f4bf890ba4179f36337f9","abstract_canon_sha256":"cd11c3c22046652e228c10c416a00898b15f8828f7f12fc1a32cd53d0981b378"},"schema_version":"1.0"},"canonical_sha256":"ced76469f9c73472fb2a6ad1f6b7976fa699b76fd451506165af97d054a127c9","source":{"kind":"arxiv","id":"2204.11041","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.11041","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"arxiv_version","alias_value":"2204.11041v3","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11041","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_12","alias_value":"Z3LWI2PZY42H","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_16","alias_value":"Z3LWI2PZY42HF6ZK","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_8","alias_value":"Z3LWI2PZ","created_at":"2026-07-05T08:01:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z3LWI2PZY42HF6ZKNLI7NN4XN6","target":"record","payload":{"canonical_record":{"source":{"id":"2204.11041","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-23T10:19:58Z","cross_cats_sorted":[],"title_canon_sha256":"58aab7315e3ef8812ee1a8cfa053752ec487fbbefd6f4bf890ba4179f36337f9","abstract_canon_sha256":"cd11c3c22046652e228c10c416a00898b15f8828f7f12fc1a32cd53d0981b378"},"schema_version":"1.0"},"canonical_sha256":"ced76469f9c73472fb2a6ad1f6b7976fa699b76fd451506165af97d054a127c9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:13.277571Z","signature_b64":"8lL/YoksGQwHSCPxdoTGI1gonUC5Xgz6p1uk46EDXUW+wk0UXom5sXBZoxV6AVI8vZImGCMN90BR+L46zg4YDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ced76469f9c73472fb2a6ad1f6b7976fa699b76fd451506165af97d054a127c9","last_reissued_at":"2026-07-05T08:01:13.277155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:13.277155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.11041","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:01:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WL+CVwvFif19eblf9qk8gzpXn+hZIEBo6k/yMp3UyV90ENJWrlSG5lzt5vFYL/oes6b5Ip864Lo6XY2cPUfzAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T09:55:15.367689Z"},"content_sha256":"76f7cd3c65d92100641575334c4cc666cf9dc863884c1fef5fc4b472ad90136f","schema_version":"1.0","event_id":"sha256:76f7cd3c65d92100641575334c4cc666cf9dc863884c1fef5fc4b472ad90136f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z3LWI2PZY42HF6ZKNLI7NN4XN6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changjae Oh, Meng Xing, Yong Su, Zhiyong Feng","submitted_at":"2022-04-23T10:19:58Z","abstract_excerpt":"Out-of-distribution (OOD) detection is essential to handle the distribution shifts between training and test scenarios. For a new in-distribution (ID) dataset, existing methods require retraining to capture the dataset-specific feature representation or data distribution. In this paper, we propose a deep generative models (DGM) based transferable OOD detection method, which is unnecessary to retrain on a new ID dataset. We design an image erasing strategy to equip exclusive conditional entropy distribution for each ID dataset, which determines the discrepancy of DGM's posteriori ucertainty dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11041","kind":"arxiv","version":3},"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/2204.11041/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":"2026-07-05T08:01:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hxkdAMiPt8ndWA/rk+rb/18wA3MC8mb1p4BMpnKvoncYFNovDwKKd5cF5j5fYiTUfTchIbwtukirP0NLxG6vAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T09:55:15.368613Z"},"content_sha256":"b810e15215e463945a421a6878847b58126629b50c9c774b8f646b567d12cc3a","schema_version":"1.0","event_id":"sha256:b810e15215e463945a421a6878847b58126629b50c9c774b8f646b567d12cc3a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/bundle.json","state_url":"https://pith.science/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/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":"2026-08-13T09:55:15Z","links":{"resolver":"https://pith.science/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6","bundle":"https://pith.science/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/bundle.json","state":"https://pith.science/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z3LWI2PZY42HF6ZKNLI7NN4XN6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z3LWI2PZY42HF6ZKNLI7NN4XN6","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":"cd11c3c22046652e228c10c416a00898b15f8828f7f12fc1a32cd53d0981b378","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-23T10:19:58Z","title_canon_sha256":"58aab7315e3ef8812ee1a8cfa053752ec487fbbefd6f4bf890ba4179f36337f9"},"schema_version":"1.0","source":{"id":"2204.11041","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.11041","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"arxiv_version","alias_value":"2204.11041v3","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11041","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_12","alias_value":"Z3LWI2PZY42H","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_16","alias_value":"Z3LWI2PZY42HF6ZK","created_at":"2026-07-05T08:01:13Z"},{"alias_kind":"pith_short_8","alias_value":"Z3LWI2PZ","created_at":"2026-07-05T08:01:13Z"}],"graph_snapshots":[{"event_id":"sha256:b810e15215e463945a421a6878847b58126629b50c9c774b8f646b567d12cc3a","target":"graph","created_at":"2026-07-05T08:01:13Z","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/2204.11041/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Out-of-distribution (OOD) detection is essential to handle the distribution shifts between training and test scenarios. For a new in-distribution (ID) dataset, existing methods require retraining to capture the dataset-specific feature representation or data distribution. In this paper, we propose a deep generative models (DGM) based transferable OOD detection method, which is unnecessary to retrain on a new ID dataset. We design an image erasing strategy to equip exclusive conditional entropy distribution for each ID dataset, which determines the discrepancy of DGM's posteriori ucertainty dis","authors_text":"Changjae Oh, Meng Xing, Yong Su, Zhiyong Feng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-23T10:19:58Z","title":"Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11041","kind":"arxiv","version":3},"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":"sha256:76f7cd3c65d92100641575334c4cc666cf9dc863884c1fef5fc4b472ad90136f","target":"record","created_at":"2026-07-05T08:01:13Z","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":"cd11c3c22046652e228c10c416a00898b15f8828f7f12fc1a32cd53d0981b378","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-23T10:19:58Z","title_canon_sha256":"58aab7315e3ef8812ee1a8cfa053752ec487fbbefd6f4bf890ba4179f36337f9"},"schema_version":"1.0","source":{"id":"2204.11041","kind":"arxiv","version":3}},"canonical_sha256":"ced76469f9c73472fb2a6ad1f6b7976fa699b76fd451506165af97d054a127c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ced76469f9c73472fb2a6ad1f6b7976fa699b76fd451506165af97d054a127c9","first_computed_at":"2026-07-05T08:01:13.277155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:01:13.277155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8lL/YoksGQwHSCPxdoTGI1gonUC5Xgz6p1uk46EDXUW+wk0UXom5sXBZoxV6AVI8vZImGCMN90BR+L46zg4YDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:01:13.277571Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.11041","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76f7cd3c65d92100641575334c4cc666cf9dc863884c1fef5fc4b472ad90136f","sha256:b810e15215e463945a421a6878847b58126629b50c9c774b8f646b567d12cc3a"],"state_sha256":"c7633cf4240cf1b6901e47f09338d7d52043a181b5ab49ec450cbe0e4cd93d32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l/QGqUB46VZYGrJS5iQ3eWrsuxFQJWOT6jSmtstN6F27fmaTM+CZbpt03g95mr8pZSQPyjIMX1Ts1Dc+iG86Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T09:55:15.374208Z","bundle_sha256":"162dc57d2a2458295aaf8ec5465fcdaf9de6bfa935dc1d61746cb4732e3d84a4"}}