{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IT6RFJVKWXH7FD7KQUTCSNO24P","short_pith_number":"pith:IT6RFJVK","schema_version":"1.0","canonical_sha256":"44fd12a6aab5cff28fea85262935dae3f2a326acaff315be75521ae50b025e14","source":{"kind":"arxiv","id":"2408.03291","version":3},"attestation_state":"computed","paper":{"title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haisong Gong, Haokun Lin, Lianwei Yang, Qingyi Gu, Yichen Wu, Zhenan Sun","submitted_at":"2024-08-06T16:40:04Z","abstract_excerpt":"Vision Transformers (ViTs) have gained significant attention, but their high computing cost limits the practical applications. While post-training quantization (PTQ) reduces model size and speeds up inference, it often degrades performance, especially in low-bit settings. We identify two key reasons for the performance degradation: 1) existing quantization methods fail to align with the power-law distribution of post-Softmax activations, and 2) reparameterizing post-LayerNorm activations leads to a performance drop due to the significant influence of outliers in the scaling factors. To address"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2408.03291","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-06T16:40:04Z","cross_cats_sorted":[],"title_canon_sha256":"664d2cf9514635244633659d8e71f669d44181481b75f217b0c3220ce8225cdf","abstract_canon_sha256":"783db263ad2c8103e47de209e55b8bb18a867b2d8eeadf4c9d46912a0bcf323d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:05.744452Z","signature_b64":"CGFhNE1wEQfDutgnRHXqFxCv9/ahzgvc1M7eQMEgrfWL/uV9XDYto+d8p9UPmwvsAi4SiOfpcPSV/YCyMgLZBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44fd12a6aab5cff28fea85262935dae3f2a326acaff315be75521ae50b025e14","last_reissued_at":"2026-07-05T11:24:05.743881Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:05.743881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haisong Gong, Haokun Lin, Lianwei Yang, Qingyi Gu, Yichen Wu, Zhenan Sun","submitted_at":"2024-08-06T16:40:04Z","abstract_excerpt":"Vision Transformers (ViTs) have gained significant attention, but their high computing cost limits the practical applications. While post-training quantization (PTQ) reduces model size and speeds up inference, it often degrades performance, especially in low-bit settings. We identify two key reasons for the performance degradation: 1) existing quantization methods fail to align with the power-law distribution of post-Softmax activations, and 2) reparameterizing post-LayerNorm activations leads to a performance drop due to the significant influence of outliers in the scaling factors. To address"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.03291","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/2408.03291/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.03291","created_at":"2026-07-05T11:24:05.743946+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.03291v3","created_at":"2026-07-05T11:24:05.743946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.03291","created_at":"2026-07-05T11:24:05.743946+00:00"},{"alias_kind":"pith_short_12","alias_value":"IT6RFJVKWXH7","created_at":"2026-07-05T11:24:05.743946+00:00"},{"alias_kind":"pith_short_16","alias_value":"IT6RFJVKWXH7FD7K","created_at":"2026-07-05T11:24:05.743946+00:00"},{"alias_kind":"pith_short_8","alias_value":"IT6RFJVK","created_at":"2026-07-05T11:24:05.743946+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"2605.31124","citing_title":"QVGGT: Post-Training Quantized Visual Geometry Grounded Transformer","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16423","citing_title":"Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2602.20309","citing_title":"QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models","ref_index":43,"is_internal_anchor":true},{"citing_arxiv_id":"2605.01330","citing_title":"Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2604.17789","citing_title":"DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P","json":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P.json","graph_json":"https://pith.science/api/pith-number/IT6RFJVKWXH7FD7KQUTCSNO24P/graph.json","events_json":"https://pith.science/api/pith-number/IT6RFJVKWXH7FD7KQUTCSNO24P/events.json","paper":"https://pith.science/paper/IT6RFJVK"},"agent_actions":{"view_html":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P","download_json":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P.json","view_paper":"https://pith.science/paper/IT6RFJVK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.03291&json=true","fetch_graph":"https://pith.science/api/pith-number/IT6RFJVKWXH7FD7KQUTCSNO24P/graph.json","fetch_events":"https://pith.science/api/pith-number/IT6RFJVKWXH7FD7KQUTCSNO24P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P/action/storage_attestation","attest_author":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P/action/author_attestation","sign_citation":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P/action/citation_signature","submit_replication":"https://pith.science/pith/IT6RFJVKWXH7FD7KQUTCSNO24P/action/replication_record"}},"created_at":"2026-07-05T11:24:05.743946+00:00","updated_at":"2026-07-05T11:24:05.743946+00:00"}