{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:6ODIK3ICTIQOKO2JU6IDR2P4BK","short_pith_number":"pith:6ODIK3IC","canonical_record":{"source":{"id":"1602.01197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-03T06:22:14Z","cross_cats_sorted":[],"title_canon_sha256":"0019c37c843ca4e75ddcc08e5b7fc619c725c0cec08225333ddc3c29bb30ba42","abstract_canon_sha256":"2340a0c20eae54abbdd74516d1d7abbc5cbc614d83c4a1f9b81770d5efab869e"},"schema_version":"1.0"},"canonical_sha256":"f386856d029a20e53b49a79038e9fc0abc8f9a2bcc1718dba9b7e18f4de73ea6","source":{"kind":"arxiv","id":"1602.01197","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.01197","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"arxiv_version","alias_value":"1602.01197v1","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.01197","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"pith_short_12","alias_value":"6ODIK3ICTIQO","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"6ODIK3ICTIQOKO2J","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"6ODIK3IC","created_at":"2026-05-18T12:30:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:6ODIK3ICTIQOKO2JU6IDR2P4BK","target":"record","payload":{"canonical_record":{"source":{"id":"1602.01197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-03T06:22:14Z","cross_cats_sorted":[],"title_canon_sha256":"0019c37c843ca4e75ddcc08e5b7fc619c725c0cec08225333ddc3c29bb30ba42","abstract_canon_sha256":"2340a0c20eae54abbdd74516d1d7abbc5cbc614d83c4a1f9b81770d5efab869e"},"schema_version":"1.0"},"canonical_sha256":"f386856d029a20e53b49a79038e9fc0abc8f9a2bcc1718dba9b7e18f4de73ea6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:20.839365Z","signature_b64":"JVdRbDDABjZowG+LWTX9/NjuMpJRR3WaCy5Y/WluCeCz68heTxVHjlKUUk71xBO78ZIj2C/u2LfJgZE7LpzCBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f386856d029a20e53b49a79038e9fc0abc8f9a2bcc1718dba9b7e18f4de73ea6","last_reissued_at":"2026-05-18T01:21:20.838776Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:20.838776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.01197","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":"2026-05-18T01:21:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y4xGyn2grpShGIP3qu9PAXck4WeMT9BnsleedB5xlveZSP160aPmcJhNYM9XDIjvZFFKplFNmaum7dYZdPSABw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:03:42.881852Z"},"content_sha256":"d03649c86962df49d0ac7de7fe6851cb8e44cdac6a150b8c7d1931a1b4dfc25a","schema_version":"1.0","event_id":"sha256:d03649c86962df49d0ac7de7fe6851cb8e44cdac6a150b8c7d1931a1b4dfc25a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:6ODIK3ICTIQOKO2JU6IDR2P4BK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Discriminative Sparse Neighbor Approximation for Imbalanced Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Chen Huang, Xiaoou Tang","submitted_at":"2016-02-03T06:22:14Z","abstract_excerpt":"Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that has a large degree of class overlap. In this study, we propose a novel discriminative sparse neighbor approximation (DSNA) method to ameliorate the effect of class-imbalance during prediction. Specifically, given a test sample, we first traverse it through a cost-sensitive decision forest to collect a g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.01197","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":""},"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":"2026-05-18T01:21:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cDMIPjyOk0HtBQiy47o069kMmEX2JjFsgn41hTyJhPYfm7vpwotBsOUMQOZawvlHA0rNq499IoUlD/g5zBzEBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T23:03:42.882190Z"},"content_sha256":"6fba2d977bb15b15decdfce681745494ed0716a8480577674812121421505ce2","schema_version":"1.0","event_id":"sha256:6fba2d977bb15b15decdfce681745494ed0716a8480577674812121421505ce2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/bundle.json","state_url":"https://pith.science/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/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":"2026-05-30T23:03:42Z","links":{"resolver":"https://pith.science/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK","bundle":"https://pith.science/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/bundle.json","state":"https://pith.science/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6ODIK3ICTIQOKO2JU6IDR2P4BK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:6ODIK3ICTIQOKO2JU6IDR2P4BK","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":"2340a0c20eae54abbdd74516d1d7abbc5cbc614d83c4a1f9b81770d5efab869e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-03T06:22:14Z","title_canon_sha256":"0019c37c843ca4e75ddcc08e5b7fc619c725c0cec08225333ddc3c29bb30ba42"},"schema_version":"1.0","source":{"id":"1602.01197","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.01197","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"arxiv_version","alias_value":"1602.01197v1","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.01197","created_at":"2026-05-18T01:21:20Z"},{"alias_kind":"pith_short_12","alias_value":"6ODIK3ICTIQO","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"6ODIK3ICTIQOKO2J","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"6ODIK3IC","created_at":"2026-05-18T12:30:01Z"}],"graph_snapshots":[{"event_id":"sha256:6fba2d977bb15b15decdfce681745494ed0716a8480577674812121421505ce2","target":"graph","created_at":"2026-05-18T01:21:20Z","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"},"paper":{"abstract_excerpt":"Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further deteriorate on small, imbalanced data that has a large degree of class overlap. In this study, we propose a novel discriminative sparse neighbor approximation (DSNA) method to ameliorate the effect of class-imbalance during prediction. Specifically, given a test sample, we first traverse it through a cost-sensitive decision forest to collect a g","authors_text":"Chen Change Loy, Chen Huang, Xiaoou Tang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-03T06:22:14Z","title":"Discriminative Sparse Neighbor Approximation for Imbalanced Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.01197","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:d03649c86962df49d0ac7de7fe6851cb8e44cdac6a150b8c7d1931a1b4dfc25a","target":"record","created_at":"2026-05-18T01:21:20Z","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":"2340a0c20eae54abbdd74516d1d7abbc5cbc614d83c4a1f9b81770d5efab869e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-03T06:22:14Z","title_canon_sha256":"0019c37c843ca4e75ddcc08e5b7fc619c725c0cec08225333ddc3c29bb30ba42"},"schema_version":"1.0","source":{"id":"1602.01197","kind":"arxiv","version":1}},"canonical_sha256":"f386856d029a20e53b49a79038e9fc0abc8f9a2bcc1718dba9b7e18f4de73ea6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f386856d029a20e53b49a79038e9fc0abc8f9a2bcc1718dba9b7e18f4de73ea6","first_computed_at":"2026-05-18T01:21:20.838776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:21:20.838776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JVdRbDDABjZowG+LWTX9/NjuMpJRR3WaCy5Y/WluCeCz68heTxVHjlKUUk71xBO78ZIj2C/u2LfJgZE7LpzCBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:21:20.839365Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.01197","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d03649c86962df49d0ac7de7fe6851cb8e44cdac6a150b8c7d1931a1b4dfc25a","sha256:6fba2d977bb15b15decdfce681745494ed0716a8480577674812121421505ce2"],"state_sha256":"dcf57f2543296f594d5a37bf6ad7c5fcb16ede6185053bc40edd26119453a899"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BOrHJVS+oueNW6PEL3MmaW30cn5n/qhgq5pqyPSKE+DO9IsJD6x8+lRjmHJNzcx1uQ17tLn00vJGUghyKjZaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T23:03:42.884167Z","bundle_sha256":"6df1fa2f5cffbcc03c15bc6098e9505fc313ef9ec21551bb696fc356c43b3973"}}