{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5HZFCN5R3YDE4KIMVD6NECP73N","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":"7abcc43f0363f4bd39e0b6e01cd0945f4aa652ae18fce3318e2c4bf816a3bcc3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-10T17:59:02Z","title_canon_sha256":"02a96ae1b09019e8d28e59d84d584548b09dfecb8977bf9692a2cdc1d2a3dc09"},"schema_version":"1.0","source":{"id":"2206.05260","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.05260","created_at":"2026-07-05T06:18:16Z"},{"alias_kind":"arxiv_version","alias_value":"2206.05260v3","created_at":"2026-07-05T06:18:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.05260","created_at":"2026-07-05T06:18:16Z"},{"alias_kind":"pith_short_12","alias_value":"5HZFCN5R3YDE","created_at":"2026-07-05T06:18:16Z"},{"alias_kind":"pith_short_16","alias_value":"5HZFCN5R3YDE4KIM","created_at":"2026-07-05T06:18:16Z"},{"alias_kind":"pith_short_8","alias_value":"5HZFCN5R","created_at":"2026-07-05T06:18:16Z"}],"graph_snapshots":[{"event_id":"sha256:bd05c57d8c761a6b0a69d1be2cccbc51c4f6a2749d1333e10cc03d1d9c1aea38","target":"graph","created_at":"2026-07-05T06:18:16Z","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/2206.05260/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work,","authors_text":"Arvi Jonnarth, Emanuel Sanchez Aimar, Marco Kuhlmann, Michael Felsberg","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-10T17:59:02Z","title":"Balanced Product of Calibrated Experts for Long-Tailed Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.05260","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":"sha256:9ddcd2deced5fdd39fbaa61b44328863de1ce82ad451b2b6f1ae4014e0300cca","target":"record","created_at":"2026-07-05T06:18:16Z","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":"7abcc43f0363f4bd39e0b6e01cd0945f4aa652ae18fce3318e2c4bf816a3bcc3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-10T17:59:02Z","title_canon_sha256":"02a96ae1b09019e8d28e59d84d584548b09dfecb8977bf9692a2cdc1d2a3dc09"},"schema_version":"1.0","source":{"id":"2206.05260","kind":"arxiv","version":3}},"canonical_sha256":"e9f25137b1de064e290ca8fcd209ffdb72a85a5f5b6c706be5a1b2f8d00b9ca7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9f25137b1de064e290ca8fcd209ffdb72a85a5f5b6c706be5a1b2f8d00b9ca7","first_computed_at":"2026-07-05T06:18:16.784453Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:18:16.784453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jMr+RWDoJynn9c7H8pwp1BeM5m+Zx/u/R9W5493s/CS8QBIrxPKJqbThO8+25e9vxScdyEO0BasAIpA9FME+AA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:18:16.784923Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.05260","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ddcd2deced5fdd39fbaa61b44328863de1ce82ad451b2b6f1ae4014e0300cca","sha256:bd05c57d8c761a6b0a69d1be2cccbc51c4f6a2749d1333e10cc03d1d9c1aea38"],"state_sha256":"3dcb6070ccff43392436072182acbe2791fc4cc36d0b0d302a5c6bfc335cd510"}