{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OOXD3S46KIWRDQIID57SGJHZIA","short_pith_number":"pith:OOXD3S46","canonical_record":{"source":{"id":"2109.04186","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-09T11:45:52Z","cross_cats_sorted":[],"title_canon_sha256":"7c58602ccb0466198e8dd48889e1c85a161e09f43735923467d636dd62db2e4f","abstract_canon_sha256":"9a005acb078f57e97b9ae4863e09ce32596ce5e17af8d781bab3927b3c1d0dd0"},"schema_version":"1.0"},"canonical_sha256":"73ae3dcb9e522d11c1081f7f2324f940220ff23b3f16e557060004169a30ac92","source":{"kind":"arxiv","id":"2109.04186","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.04186","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2109.04186v3","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04186","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"OOXD3S46KIWR","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"OOXD3S46KIWRDQII","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"OOXD3S46","created_at":"2026-07-05T04:37:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OOXD3S46KIWRDQIID57SGJHZIA","target":"record","payload":{"canonical_record":{"source":{"id":"2109.04186","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-09T11:45:52Z","cross_cats_sorted":[],"title_canon_sha256":"7c58602ccb0466198e8dd48889e1c85a161e09f43735923467d636dd62db2e4f","abstract_canon_sha256":"9a005acb078f57e97b9ae4863e09ce32596ce5e17af8d781bab3927b3c1d0dd0"},"schema_version":"1.0"},"canonical_sha256":"73ae3dcb9e522d11c1081f7f2324f940220ff23b3f16e557060004169a30ac92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:37:18.341405Z","signature_b64":"WtuLRSCTmDlnV+3YE9Sg5nUrmR4zQCQyWpsip4KZbtNndoHpj6YM9IGQYjoc1+olWYvBEWwWDUE9uq61WeDODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73ae3dcb9e522d11c1081f7f2324f940220ff23b3f16e557060004169a30ac92","last_reissued_at":"2026-07-05T04:37:18.340971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:37:18.340971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.04186","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":"2026-07-05T04:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2BjQptuZ6BL2fcxAQIl0u6wLi1Qh4qFVtyyixku5fSnfiREW8YroZTkj0bKU+HI4M307/o60/6qZqFhpzWJjDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:34:49.915803Z"},"content_sha256":"90dee51d3c017ce5729972a258223a6293972ecac6376b6c8d0e6bd1a278e5fc","schema_version":"1.0","event_id":"sha256:90dee51d3c017ce5729972a258223a6293972ecac6376b6c8d0e6bd1a278e5fc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OOXD3S46KIWRDQIID57SGJHZIA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-grained Data Distribution Alignment for Post-Training Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fei Chao, Ke Li, Mengzhao Chen, Mingbao Lin, Rongrong Ji, Yongjian Wu, Yunhang Shen, Yunshan Zhong","submitted_at":"2021-09-09T11:45:52Z","abstract_excerpt":"While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images. To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization. The method is based on two important properties of batch normalization statistics (BNS) we observed in deep layers of the trained network, (i.e.), inter-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04186","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/2109.04186/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":"2026-07-05T04:37:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UKGF7DqFVbeFlMg4vBwVZwUWFYCNhP7TlIwKf2cGJgTVRcDQ7tnML2GYEFb3CvX01dvfYD5eRhyguwbRGy/ZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:34:49.916298Z"},"content_sha256":"7fe267616760e48498a2ee341544c5a78944583417ddb81e26f9f88860ee66a1","schema_version":"1.0","event_id":"sha256:7fe267616760e48498a2ee341544c5a78944583417ddb81e26f9f88860ee66a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OOXD3S46KIWRDQIID57SGJHZIA/bundle.json","state_url":"https://pith.science/pith/OOXD3S46KIWRDQIID57SGJHZIA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OOXD3S46KIWRDQIID57SGJHZIA/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":"2026-08-12T14:34:49Z","links":{"resolver":"https://pith.science/pith/OOXD3S46KIWRDQIID57SGJHZIA","bundle":"https://pith.science/pith/OOXD3S46KIWRDQIID57SGJHZIA/bundle.json","state":"https://pith.science/pith/OOXD3S46KIWRDQIID57SGJHZIA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OOXD3S46KIWRDQIID57SGJHZIA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OOXD3S46KIWRDQIID57SGJHZIA","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":"9a005acb078f57e97b9ae4863e09ce32596ce5e17af8d781bab3927b3c1d0dd0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-09T11:45:52Z","title_canon_sha256":"7c58602ccb0466198e8dd48889e1c85a161e09f43735923467d636dd62db2e4f"},"schema_version":"1.0","source":{"id":"2109.04186","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.04186","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"arxiv_version","alias_value":"2109.04186v3","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04186","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_12","alias_value":"OOXD3S46KIWR","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_16","alias_value":"OOXD3S46KIWRDQII","created_at":"2026-07-05T04:37:18Z"},{"alias_kind":"pith_short_8","alias_value":"OOXD3S46","created_at":"2026-07-05T04:37:18Z"}],"graph_snapshots":[{"event_id":"sha256:7fe267616760e48498a2ee341544c5a78944583417ddb81e26f9f88860ee66a1","target":"graph","created_at":"2026-07-05T04:37:18Z","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/2109.04186/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce images. To alleviate this limitation, in this paper, we leverage the synthetic data introduced by zero-shot quantization with calibration dataset and propose a fine-grained data distribution alignment (FDDA) method to boost the performance of post-training quantization. The method is based on two important properties of batch normalization statistics (BNS) we observed in deep layers of the trained network, (i.e.), inter-","authors_text":"Fei Chao, Ke Li, Mengzhao Chen, Mingbao Lin, Rongrong Ji, Yongjian Wu, Yunhang Shen, Yunshan Zhong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-09T11:45:52Z","title":"Fine-grained Data Distribution Alignment for Post-Training Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04186","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":"sha256:90dee51d3c017ce5729972a258223a6293972ecac6376b6c8d0e6bd1a278e5fc","target":"record","created_at":"2026-07-05T04:37:18Z","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":"9a005acb078f57e97b9ae4863e09ce32596ce5e17af8d781bab3927b3c1d0dd0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-09T11:45:52Z","title_canon_sha256":"7c58602ccb0466198e8dd48889e1c85a161e09f43735923467d636dd62db2e4f"},"schema_version":"1.0","source":{"id":"2109.04186","kind":"arxiv","version":3}},"canonical_sha256":"73ae3dcb9e522d11c1081f7f2324f940220ff23b3f16e557060004169a30ac92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73ae3dcb9e522d11c1081f7f2324f940220ff23b3f16e557060004169a30ac92","first_computed_at":"2026-07-05T04:37:18.340971Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:37:18.340971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WtuLRSCTmDlnV+3YE9Sg5nUrmR4zQCQyWpsip4KZbtNndoHpj6YM9IGQYjoc1+olWYvBEWwWDUE9uq61WeDODA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:37:18.341405Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.04186","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90dee51d3c017ce5729972a258223a6293972ecac6376b6c8d0e6bd1a278e5fc","sha256:7fe267616760e48498a2ee341544c5a78944583417ddb81e26f9f88860ee66a1"],"state_sha256":"00ce3dfd35e694a383f6e7c7366fd48a0856df0f66f7dee6d95da90950f109e1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NMsYN+chR7dxdf81VsUyyLVQR7yR1PdzafiKEg7VDxrh5//BSKmvQYsjVyCeDZ+CWIjPA300dAa1eDBFNhs2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T14:34:49.920116Z","bundle_sha256":"ae8ce71cbf65a9c89b6557b7512bb187d3f533b66f90c08f5ef7ccdda13d5da1"}}