{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VLWX6OOGHTEAWRMK3STE6T5WEB","short_pith_number":"pith:VLWX6OOG","schema_version":"1.0","canonical_sha256":"aaed7f39c63cc80b458adca64f4fb6207d64c465a30e9e74e8620298f5ef0075","source":{"kind":"arxiv","id":"1901.09135","version":1},"attestation_state":"computed","paper":{"title":"Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Alexander Wong, Zhong Qiu Lin","submitted_at":"2019-01-26T01:22:14Z","abstract_excerpt":"Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate t"},"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":"1901.09135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-26T01:22:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1ce0281e61540c39e59578483da7f7cb3db8614e5865d984ea611cbd911850f6","abstract_canon_sha256":"0b2366246a0c62fbdd7fb3f32890e90441507ab024d8f4bd9119940f056fce87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:30.451958Z","signature_b64":"5ErZYtIIpUq3M8BeE8Z0xigkXP0oEOXLqXQhogc/qGDA8J/tfKJr4Ha1CFaP3v2UVHeHAXhommaftESjBJuSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaed7f39c63cc80b458adca64f4fb6207d64c465a30e9e74e8620298f5ef0075","last_reissued_at":"2026-05-17T23:55:30.451472Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:30.451472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Alexander Wong, Zhong Qiu Lin","submitted_at":"2019-01-26T01:22:14Z","abstract_excerpt":"Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09135","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":"1901.09135","created_at":"2026-05-17T23:55:30.451546+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09135v1","created_at":"2026-05-17T23:55:30.451546+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09135","created_at":"2026-05-17T23:55:30.451546+00:00"},{"alias_kind":"pith_short_12","alias_value":"VLWX6OOGHTEA","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VLWX6OOGHTEAWRMK","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VLWX6OOG","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB","json":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB.json","graph_json":"https://pith.science/api/pith-number/VLWX6OOGHTEAWRMK3STE6T5WEB/graph.json","events_json":"https://pith.science/api/pith-number/VLWX6OOGHTEAWRMK3STE6T5WEB/events.json","paper":"https://pith.science/paper/VLWX6OOG"},"agent_actions":{"view_html":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB","download_json":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB.json","view_paper":"https://pith.science/paper/VLWX6OOG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09135&json=true","fetch_graph":"https://pith.science/api/pith-number/VLWX6OOGHTEAWRMK3STE6T5WEB/graph.json","fetch_events":"https://pith.science/api/pith-number/VLWX6OOGHTEAWRMK3STE6T5WEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB/action/storage_attestation","attest_author":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB/action/author_attestation","sign_citation":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB/action/citation_signature","submit_replication":"https://pith.science/pith/VLWX6OOGHTEAWRMK3STE6T5WEB/action/replication_record"}},"created_at":"2026-05-17T23:55:30.451546+00:00","updated_at":"2026-05-17T23:55:30.451546+00:00"}