{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5Z4AWVJTH2T3FM7DIJMDZQ6CAS","short_pith_number":"pith:5Z4AWVJT","schema_version":"1.0","canonical_sha256":"ee780b55333ea7b2b3e342583cc3c204a365a260cfd36032e0516bd2dc64a7b9","source":{"kind":"arxiv","id":"1704.00648","version":2},"attestation_state":"computed","paper":{"title":"Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Eirikur Agustsson, Fabian Mentzer, Luca Benini, Luc Van Gool, Lukas Cavigelli, Michael Tschannen, Radu Timofte","submitted_at":"2017-04-03T15:39:56Z","abstract_excerpt":"We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both."},"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":"1704.00648","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-03T15:39:56Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"13170517474045ddefcd84cfe73eb3b32c6bf732cb62462c60547f0a89cdec8e","abstract_canon_sha256":"2ed15237fb666a5346cb86db9d355d0127255e635c37d9a28306b3ca3a5fc54e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:45.984148Z","signature_b64":"3lf6zQ9TME4J4KfIj1sIf9luY2J8JKzdTWmMxFL6/89glszniwxHjsDPN66ktRHFLC5jqEfdQEFJhxNrrjxTBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee780b55333ea7b2b3e342583cc3c204a365a260cfd36032e0516bd2dc64a7b9","last_reissued_at":"2026-05-18T00:42:45.983498Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:45.983498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Eirikur Agustsson, Fabian Mentzer, Luca Benini, Luc Van Gool, Lukas Cavigelli, Michael Tschannen, Radu Timofte","submitted_at":"2017-04-03T15:39:56Z","abstract_excerpt":"We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00648","kind":"arxiv","version":2},"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":"1704.00648","created_at":"2026-05-18T00:42:45.983601+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.00648v2","created_at":"2026-05-18T00:42:45.983601+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00648","created_at":"2026-05-18T00:42:45.983601+00:00"},{"alias_kind":"pith_short_12","alias_value":"5Z4AWVJTH2T3","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"5Z4AWVJTH2T3FM7D","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"5Z4AWVJT","created_at":"2026-05-18T12:31:03.183658+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1906.09683","citing_title":"Learning Image and Video Compression through Spatial-Temporal Energy Compaction","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"1906.09731","citing_title":"Deep Residual Learning for Image Compression","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2208.01618","citing_title":"An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS","json":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS.json","graph_json":"https://pith.science/api/pith-number/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/graph.json","events_json":"https://pith.science/api/pith-number/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/events.json","paper":"https://pith.science/paper/5Z4AWVJT"},"agent_actions":{"view_html":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS","download_json":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS.json","view_paper":"https://pith.science/paper/5Z4AWVJT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.00648&json=true","fetch_graph":"https://pith.science/api/pith-number/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/graph.json","fetch_events":"https://pith.science/api/pith-number/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/action/storage_attestation","attest_author":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/action/author_attestation","sign_citation":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/action/citation_signature","submit_replication":"https://pith.science/pith/5Z4AWVJTH2T3FM7DIJMDZQ6CAS/action/replication_record"}},"created_at":"2026-05-18T00:42:45.983601+00:00","updated_at":"2026-05-18T00:42:45.983601+00:00"}