{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:X7Q63AN5PEC4M67WR2GYG7KZVC","short_pith_number":"pith:X7Q63AN5","canonical_record":{"source":{"id":"2102.01906","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-03T06:54:52Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"871485acab070e700f3015e8ac406ae4f6335beeea6e6f7915b959e641745a0d","abstract_canon_sha256":"4da5cf9c720940c44ce97a1514252938b7957f9f06bf8a562e551322d6a36e2e"},"schema_version":"1.0"},"canonical_sha256":"bfe1ed81bd7905c67bf68e8d837d59a898216309747177aa83400343c9139d39","source":{"kind":"arxiv","id":"2102.01906","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.01906","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.01906v1","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.01906","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_12","alias_value":"X7Q63AN5PEC4","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_16","alias_value":"X7Q63AN5PEC4M67W","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_8","alias_value":"X7Q63AN5","created_at":"2026-07-05T02:12:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:X7Q63AN5PEC4M67WR2GYG7KZVC","target":"record","payload":{"canonical_record":{"source":{"id":"2102.01906","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-03T06:54:52Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"871485acab070e700f3015e8ac406ae4f6335beeea6e6f7915b959e641745a0d","abstract_canon_sha256":"4da5cf9c720940c44ce97a1514252938b7957f9f06bf8a562e551322d6a36e2e"},"schema_version":"1.0"},"canonical_sha256":"bfe1ed81bd7905c67bf68e8d837d59a898216309747177aa83400343c9139d39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:12:34.728756Z","signature_b64":"MoZ766Z+uUenKuqGEyiorvHMYoZBSSk9L6+oDnypbk7rqehgTiczSclH3zQ6NL1Y+6NjDKhU0y8XOfTRaGpQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfe1ed81bd7905c67bf68e8d837d59a898216309747177aa83400343c9139d39","last_reissued_at":"2026-07-05T02:12:34.728346Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:12:34.728346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.01906","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":"2026-07-05T02:12:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dyuPB4ZkX7K7KOlH5C6KbAWYozm67vL0oe8cZzGXCGfLoQZ+yEiwdBuj+WZNnlozJYL+6Be4O6UezKLcceJpBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T18:40:09.289449Z"},"content_sha256":"6cc75dc7952314e88c7114b48c7df6c321f510d124e8d6ba13d5382b2db85d2f","schema_version":"1.0","event_id":"sha256:6cc75dc7952314e88c7114b48c7df6c321f510d124e8d6ba13d5382b2db85d2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:X7Q63AN5PEC4M67WR2GYG7KZVC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Badri N. Patro, Venkatesh K. Subramanian, Vinay P. Namboodiri, Vinod K Kurmi","submitted_at":"2021-02-03T06:54:52Z","abstract_excerpt":"One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task models, such as uncertainty estimation in predictions. The predictive uncertainty provides the distributional information can be applied to mitigate catastrophic forgetting in a deep learning framework. In the proposed work, we consider a Bayesian fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.01906","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/2102.01906/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":"2026-07-05T02:12:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wvdUg8mlwigXzn90U+Wzz0iGfmqmpMW5nW3ndwFc87MdoUaLSuYHcjltG2WBtzcBjc1C4eDMZ1HR1lN0gKVRAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T18:40:09.289830Z"},"content_sha256":"67cf8cd169a6e1c62f67ff00f9d158220715051ffdd29a951730ed088731fec8","schema_version":"1.0","event_id":"sha256:67cf8cd169a6e1c62f67ff00f9d158220715051ffdd29a951730ed088731fec8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/bundle.json","state_url":"https://pith.science/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/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":"2026-07-12T18:40:09Z","links":{"resolver":"https://pith.science/pith/X7Q63AN5PEC4M67WR2GYG7KZVC","bundle":"https://pith.science/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/bundle.json","state":"https://pith.science/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X7Q63AN5PEC4M67WR2GYG7KZVC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:X7Q63AN5PEC4M67WR2GYG7KZVC","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":"4da5cf9c720940c44ce97a1514252938b7957f9f06bf8a562e551322d6a36e2e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-03T06:54:52Z","title_canon_sha256":"871485acab070e700f3015e8ac406ae4f6335beeea6e6f7915b959e641745a0d"},"schema_version":"1.0","source":{"id":"2102.01906","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.01906","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"arxiv_version","alias_value":"2102.01906v1","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.01906","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_12","alias_value":"X7Q63AN5PEC4","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_16","alias_value":"X7Q63AN5PEC4M67W","created_at":"2026-07-05T02:12:34Z"},{"alias_kind":"pith_short_8","alias_value":"X7Q63AN5","created_at":"2026-07-05T02:12:34Z"}],"graph_snapshots":[{"event_id":"sha256:67cf8cd169a6e1c62f67ff00f9d158220715051ffdd29a951730ed088731fec8","target":"graph","created_at":"2026-07-05T02:12:34Z","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/2102.01906/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task models, such as uncertainty estimation in predictions. The predictive uncertainty provides the distributional information can be applied to mitigate catastrophic forgetting in a deep learning framework. In the proposed work, we consider a Bayesian fo","authors_text":"Badri N. Patro, Venkatesh K. Subramanian, Vinay P. Namboodiri, Vinod K Kurmi","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-03T06:54:52Z","title":"Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.01906","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":"sha256:6cc75dc7952314e88c7114b48c7df6c321f510d124e8d6ba13d5382b2db85d2f","target":"record","created_at":"2026-07-05T02:12:34Z","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":"4da5cf9c720940c44ce97a1514252938b7957f9f06bf8a562e551322d6a36e2e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-03T06:54:52Z","title_canon_sha256":"871485acab070e700f3015e8ac406ae4f6335beeea6e6f7915b959e641745a0d"},"schema_version":"1.0","source":{"id":"2102.01906","kind":"arxiv","version":1}},"canonical_sha256":"bfe1ed81bd7905c67bf68e8d837d59a898216309747177aa83400343c9139d39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfe1ed81bd7905c67bf68e8d837d59a898216309747177aa83400343c9139d39","first_computed_at":"2026-07-05T02:12:34.728346Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:12:34.728346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MoZ766Z+uUenKuqGEyiorvHMYoZBSSk9L6+oDnypbk7rqehgTiczSclH3zQ6NL1Y+6NjDKhU0y8XOfTRaGpQAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:12:34.728756Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.01906","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6cc75dc7952314e88c7114b48c7df6c321f510d124e8d6ba13d5382b2db85d2f","sha256:67cf8cd169a6e1c62f67ff00f9d158220715051ffdd29a951730ed088731fec8"],"state_sha256":"82e0e971bac710ece28c1dd4081d157c041c8dd73dfce3b68761e48736b5ebc2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jj4u0OhkyXXP4aS2wWeatttlL+1/SIqylNbizRJrwLM/5we3Vi9TEbMRSPjbCU+7NO58jv6/gwngJtNPoAUXBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T18:40:09.292225Z","bundle_sha256":"04ed971b1afdc6a75fc3acc5f68246cf9f9924a42b2b1255e33f14ecdff53a63"}}