{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZQKJNDAJE4IOHWWRDDD7USFW22","short_pith_number":"pith:ZQKJNDAJ","schema_version":"1.0","canonical_sha256":"cc14968c092710e3dad118c7fa48b6d6b57bf469f9e1e92021237619a15e152c","source":{"kind":"arxiv","id":"1711.05852","version":1},"attestation_state":"computed","paper":{"title":"Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Asit Mishra, Debbie Marr","submitted_at":"2017-11-15T23:45:59Z","abstract_excerpt":"Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems - the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footpri"},"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":"1711.05852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-15T23:45:59Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"c404b70a0442f27c2febc338ccd2ac116abe68f7ef978cf6a4f7e5da2e17a3b3","abstract_canon_sha256":"e738769502611c91294f55d9217664072eae090a5bd49152215e4fcd291db313"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:23.959516Z","signature_b64":"1KWnXqnOG+V861Whk/8b65aq3TV+cKtcdasfugex4nICwM8eqx+eZJu5Qke5VWBgT2Ew7V1xHg5u89cgUFLdBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc14968c092710e3dad118c7fa48b6d6b57bf469f9e1e92021237619a15e152c","last_reissued_at":"2026-05-18T00:30:23.958967Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:23.958967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Asit Mishra, Debbie Marr","submitted_at":"2017-11-15T23:45:59Z","abstract_excerpt":"Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems - the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footpri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05852","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.05852","created_at":"2026-05-18T00:30:23.959046+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05852v1","created_at":"2026-05-18T00:30:23.959046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05852","created_at":"2026-05-18T00:30:23.959046+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQKJNDAJE4IO","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQKJNDAJE4IOHWWR","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQKJNDAJ","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2112.11447","citing_title":"Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22","json":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22.json","graph_json":"https://pith.science/api/pith-number/ZQKJNDAJE4IOHWWRDDD7USFW22/graph.json","events_json":"https://pith.science/api/pith-number/ZQKJNDAJE4IOHWWRDDD7USFW22/events.json","paper":"https://pith.science/paper/ZQKJNDAJ"},"agent_actions":{"view_html":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22","download_json":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22.json","view_paper":"https://pith.science/paper/ZQKJNDAJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05852&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQKJNDAJE4IOHWWRDDD7USFW22/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQKJNDAJE4IOHWWRDDD7USFW22/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22/action/storage_attestation","attest_author":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22/action/author_attestation","sign_citation":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22/action/citation_signature","submit_replication":"https://pith.science/pith/ZQKJNDAJE4IOHWWRDDD7USFW22/action/replication_record"}},"created_at":"2026-05-18T00:30:23.959046+00:00","updated_at":"2026-05-18T00:30:23.959046+00:00"}