{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6AGE5K4QXGH2XYTEQQHB3LO7GQ","short_pith_number":"pith:6AGE5K4Q","canonical_record":{"source":{"id":"2106.14156","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-27T06:27:22Z","cross_cats_sorted":[],"title_canon_sha256":"10f166479f6cbf0fde30d3e19107987450bc4a6d93ad8357a01883737eccf408","abstract_canon_sha256":"f9eb2627eff982e91d06d96ad34596a980b77b6894d9c92cf67590d3bb5babef"},"schema_version":"1.0"},"canonical_sha256":"f00c4eab90b98fabe264840e1daddf3431bea45d57accc612d33147635149001","source":{"kind":"arxiv","id":"2106.14156","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.14156","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"arxiv_version","alias_value":"2106.14156v1","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.14156","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_12","alias_value":"6AGE5K4QXGH2","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_16","alias_value":"6AGE5K4QXGH2XYTE","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_8","alias_value":"6AGE5K4Q","created_at":"2026-07-05T02:52:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6AGE5K4QXGH2XYTEQQHB3LO7GQ","target":"record","payload":{"canonical_record":{"source":{"id":"2106.14156","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-27T06:27:22Z","cross_cats_sorted":[],"title_canon_sha256":"10f166479f6cbf0fde30d3e19107987450bc4a6d93ad8357a01883737eccf408","abstract_canon_sha256":"f9eb2627eff982e91d06d96ad34596a980b77b6894d9c92cf67590d3bb5babef"},"schema_version":"1.0"},"canonical_sha256":"f00c4eab90b98fabe264840e1daddf3431bea45d57accc612d33147635149001","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:52:47.920073Z","signature_b64":"xMpkvzlYblfs75xGukEXtk9RpGQEWZVTGtYdSOxqQ/7S8rYlZ/J4l9m1SwV19ZcZTUBgNnnAyWcX2rzkFSeFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f00c4eab90b98fabe264840e1daddf3431bea45d57accc612d33147635149001","last_reissued_at":"2026-07-05T02:52:47.919561Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:52:47.919561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.14156","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":"2026-07-05T02:52:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hLeh62qS6uLfyG5utUSJtAj2gW0NaJd10T1F5MQUI45Zed8ylWoffvmeDyNGtVXTwYN87IzMUl5MfZGcU/9BCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:07:11.514095Z"},"content_sha256":"967ffd7a3115c8594c1bb69ee6e0235bbd7d99a5d9773bb3d2c466a118cf6624","schema_version":"1.0","event_id":"sha256:967ffd7a3115c8594c1bb69ee6e0235bbd7d99a5d9773bb3d2c466a118cf6624"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6AGE5K4QXGH2XYTEQQHB3LO7GQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Post-Training Quantization for Vision Transformer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kai Han, Siwei Ma, Wen Gao, Yunhe Wang, Zhenhua Liu","submitted_at":"2021-06-27T06:27:22Z","abstract_excerpt":"Recently, transformer has achieved remarkable performance on a variety of computer vision applications. Compared with mainstream convolutional neural networks, vision transformers are often of sophisticated architectures for extracting powerful feature representations, which are more difficult to be developed on mobile devices. In this paper, we present an effective post-training quantization algorithm for reducing the memory storage and computational costs of vision transformers. Basically, the quantization task can be regarded as finding the optimal low-bit quantization intervals for weights"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.14156","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/2106.14156/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":"2026-07-05T02:52:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MBMf+7sMxHHjB7j84Jbzw/JGgxkOGCGXeSbBDKGjxGXfcq4oxG7I+Sf4OMec+yWZe+VmuzDf229m8fggNrpLBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T16:07:11.514654Z"},"content_sha256":"0dde6d0001495577788cb5481cd3a5bca20acea593687eae1334d14e3203da5e","schema_version":"1.0","event_id":"sha256:0dde6d0001495577788cb5481cd3a5bca20acea593687eae1334d14e3203da5e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/bundle.json","state_url":"https://pith.science/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/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":"2026-08-03T16:07:11Z","links":{"resolver":"https://pith.science/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ","bundle":"https://pith.science/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/bundle.json","state":"https://pith.science/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6AGE5K4QXGH2XYTEQQHB3LO7GQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6AGE5K4QXGH2XYTEQQHB3LO7GQ","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":"f9eb2627eff982e91d06d96ad34596a980b77b6894d9c92cf67590d3bb5babef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-27T06:27:22Z","title_canon_sha256":"10f166479f6cbf0fde30d3e19107987450bc4a6d93ad8357a01883737eccf408"},"schema_version":"1.0","source":{"id":"2106.14156","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.14156","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"arxiv_version","alias_value":"2106.14156v1","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.14156","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_12","alias_value":"6AGE5K4QXGH2","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_16","alias_value":"6AGE5K4QXGH2XYTE","created_at":"2026-07-05T02:52:47Z"},{"alias_kind":"pith_short_8","alias_value":"6AGE5K4Q","created_at":"2026-07-05T02:52:47Z"}],"graph_snapshots":[{"event_id":"sha256:0dde6d0001495577788cb5481cd3a5bca20acea593687eae1334d14e3203da5e","target":"graph","created_at":"2026-07-05T02:52:47Z","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/2106.14156/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, transformer has achieved remarkable performance on a variety of computer vision applications. Compared with mainstream convolutional neural networks, vision transformers are often of sophisticated architectures for extracting powerful feature representations, which are more difficult to be developed on mobile devices. In this paper, we present an effective post-training quantization algorithm for reducing the memory storage and computational costs of vision transformers. Basically, the quantization task can be regarded as finding the optimal low-bit quantization intervals for weights","authors_text":"Kai Han, Siwei Ma, Wen Gao, Yunhe Wang, Zhenhua Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-27T06:27:22Z","title":"Post-Training Quantization for Vision Transformer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.14156","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":"sha256:967ffd7a3115c8594c1bb69ee6e0235bbd7d99a5d9773bb3d2c466a118cf6624","target":"record","created_at":"2026-07-05T02:52:47Z","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":"f9eb2627eff982e91d06d96ad34596a980b77b6894d9c92cf67590d3bb5babef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-06-27T06:27:22Z","title_canon_sha256":"10f166479f6cbf0fde30d3e19107987450bc4a6d93ad8357a01883737eccf408"},"schema_version":"1.0","source":{"id":"2106.14156","kind":"arxiv","version":1}},"canonical_sha256":"f00c4eab90b98fabe264840e1daddf3431bea45d57accc612d33147635149001","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f00c4eab90b98fabe264840e1daddf3431bea45d57accc612d33147635149001","first_computed_at":"2026-07-05T02:52:47.919561Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:52:47.919561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xMpkvzlYblfs75xGukEXtk9RpGQEWZVTGtYdSOxqQ/7S8rYlZ/J4l9m1SwV19ZcZTUBgNnnAyWcX2rzkFSeFDw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:52:47.920073Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.14156","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:967ffd7a3115c8594c1bb69ee6e0235bbd7d99a5d9773bb3d2c466a118cf6624","sha256:0dde6d0001495577788cb5481cd3a5bca20acea593687eae1334d14e3203da5e"],"state_sha256":"d6c5b3b7ba0eb71301405aa1ced3a44e24d773820b35a96e3ba4e3d45ed07aef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CKlyzRqQRFyP39A9h+9hsG3rggQ1cGEt7s+ftIIdvuYU1N1JttheWHDirtw3u4hMaUEqbAJm2JhyaRWXG1vkBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T16:07:11.520213Z","bundle_sha256":"5566ce3f0e088d4c0a6e8a9064503a4b9bef4a2e2a3327a8a4753104e9f24942"}}