{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:J7GESABAQ4PK2Z7NTFOLCSAZDJ","short_pith_number":"pith:J7GESABA","canonical_record":{"source":{"id":"2112.08810","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-16T11:51:35Z","cross_cats_sorted":[],"title_canon_sha256":"2348e37dbe041035cb6c7b3dddb6923f4929d0c31ae92cb45c5cad1ea5b10840","abstract_canon_sha256":"d4c077edc4baac403b3d076f21b31d7037ee292d7d71c1fbb314a5bbc1877908"},"schema_version":"1.0"},"canonical_sha256":"4fcc490020871ead67ed995cb148191a4e9f219cff868aafff99ac0b90fb1262","source":{"kind":"arxiv","id":"2112.08810","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08810","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08810v2","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08810","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_12","alias_value":"J7GESABAQ4PK","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"J7GESABAQ4PK2Z7N","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"J7GESABA","created_at":"2026-07-05T04:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:J7GESABAQ4PK2Z7NTFOLCSAZDJ","target":"record","payload":{"canonical_record":{"source":{"id":"2112.08810","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-16T11:51:35Z","cross_cats_sorted":[],"title_canon_sha256":"2348e37dbe041035cb6c7b3dddb6923f4929d0c31ae92cb45c5cad1ea5b10840","abstract_canon_sha256":"d4c077edc4baac403b3d076f21b31d7037ee292d7d71c1fbb314a5bbc1877908"},"schema_version":"1.0"},"canonical_sha256":"4fcc490020871ead67ed995cb148191a4e9f219cff868aafff99ac0b90fb1262","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:32:53.997726Z","signature_b64":"vVi3iRUayhMuy3R+pBl577ORgiObTIHcX7ipTPjF//pQsJZRtg7Xz/YDZ1kuVLzCPOCxvCsNVxP2mhY9VBnxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fcc490020871ead67ed995cb148191a4e9f219cff868aafff99ac0b90fb1262","last_reissued_at":"2026-07-05T04:32:53.997273Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:32:53.997273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.08810","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:32:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7kI8fEVIt+GJNvfGBXUQmuWLCrpyNgtnq3qyMsPu935InHeyuFEM4qnv0XmNkEeAHV8/Og/jSEocpI16oqNfDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:12:14.113502Z"},"content_sha256":"e513dd42ca4a9ee3647800e0f49a974dc47d943944a9ce990013e8b48332d23d","schema_version":"1.0","event_id":"sha256:e513dd42ca4a9ee3647800e0f49a974dc47d943944a9ce990013e8b48332d23d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:J7GESABAQ4PK2Z7NTFOLCSAZDJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Itay Benou, Michal Irani, Shiran Zada","submitted_at":"2021-12-16T11:51:35Z","abstract_excerpt":"Despite remarkable progress on visual recognition tasks, deep neural-nets still struggle to generalize well when training data is scarce or highly imbalanced, rendering them extremely vulnerable to real-world examples. In this paper, we present a surprisingly simple yet highly effective method to mitigate this limitation: using pure noise images as additional training data. Unlike the common use of additive noise or adversarial noise for data augmentation, we propose an entirely different perspective by directly training on pure random noise images. We present a new Distribution-Aware Routing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08810","kind":"arxiv","version":2},"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/2112.08810/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":"2026-07-05T04:32:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FxNZw/6Xq/Y8HtHRrSzfOvCLbN8P92yZ+QlCT8hJtap+8huz/FX8V2v5++p1b1rsMVjFd3aLonVqt8doYFCsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:12:14.113879Z"},"content_sha256":"68d729bda738bc59e35d748dfb0de80032cf404b74fbc11961074f4f853ce3a3","schema_version":"1.0","event_id":"sha256:68d729bda738bc59e35d748dfb0de80032cf404b74fbc11961074f4f853ce3a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/bundle.json","state_url":"https://pith.science/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/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":"2026-07-06T23:12:14Z","links":{"resolver":"https://pith.science/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ","bundle":"https://pith.science/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/bundle.json","state":"https://pith.science/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J7GESABAQ4PK2Z7NTFOLCSAZDJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:J7GESABAQ4PK2Z7NTFOLCSAZDJ","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":"d4c077edc4baac403b3d076f21b31d7037ee292d7d71c1fbb314a5bbc1877908","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-16T11:51:35Z","title_canon_sha256":"2348e37dbe041035cb6c7b3dddb6923f4929d0c31ae92cb45c5cad1ea5b10840"},"schema_version":"1.0","source":{"id":"2112.08810","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08810","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08810v2","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08810","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_12","alias_value":"J7GESABAQ4PK","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"J7GESABAQ4PK2Z7N","created_at":"2026-07-05T04:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"J7GESABA","created_at":"2026-07-05T04:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:68d729bda738bc59e35d748dfb0de80032cf404b74fbc11961074f4f853ce3a3","target":"graph","created_at":"2026-07-05T04:32:53Z","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/2112.08810/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite remarkable progress on visual recognition tasks, deep neural-nets still struggle to generalize well when training data is scarce or highly imbalanced, rendering them extremely vulnerable to real-world examples. In this paper, we present a surprisingly simple yet highly effective method to mitigate this limitation: using pure noise images as additional training data. Unlike the common use of additive noise or adversarial noise for data augmentation, we propose an entirely different perspective by directly training on pure random noise images. We present a new Distribution-Aware Routing ","authors_text":"Itay Benou, Michal Irani, Shiran Zada","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-16T11:51:35Z","title":"Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08810","kind":"arxiv","version":2},"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:e513dd42ca4a9ee3647800e0f49a974dc47d943944a9ce990013e8b48332d23d","target":"record","created_at":"2026-07-05T04:32:53Z","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":"d4c077edc4baac403b3d076f21b31d7037ee292d7d71c1fbb314a5bbc1877908","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-12-16T11:51:35Z","title_canon_sha256":"2348e37dbe041035cb6c7b3dddb6923f4929d0c31ae92cb45c5cad1ea5b10840"},"schema_version":"1.0","source":{"id":"2112.08810","kind":"arxiv","version":2}},"canonical_sha256":"4fcc490020871ead67ed995cb148191a4e9f219cff868aafff99ac0b90fb1262","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4fcc490020871ead67ed995cb148191a4e9f219cff868aafff99ac0b90fb1262","first_computed_at":"2026-07-05T04:32:53.997273Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:32:53.997273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vVi3iRUayhMuy3R+pBl577ORgiObTIHcX7ipTPjF//pQsJZRtg7Xz/YDZ1kuVLzCPOCxvCsNVxP2mhY9VBnxAA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:32:53.997726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.08810","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e513dd42ca4a9ee3647800e0f49a974dc47d943944a9ce990013e8b48332d23d","sha256:68d729bda738bc59e35d748dfb0de80032cf404b74fbc11961074f4f853ce3a3"],"state_sha256":"70e88bac670711e36b73e286a3d544b40b8ec13554aab0e6a75413a8e294f3e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JUMMm5RHlQEb1wXu8eCW7EK1/CWoBjvdZmK0pyhRmXjpXIO6r964ESS9qjhd0Mes/92Fq5KFch2zyOt4s+Z6Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:12:14.115825Z","bundle_sha256":"aa41dcd2d7e8e2abf8ca45805884846914e55fbc47f361d3e145104e14c9470d"}}