{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6TSXWNCGVQCTNOAZML4GFA2TV7","short_pith_number":"pith:6TSXWNCG","schema_version":"1.0","canonical_sha256":"f4e57b3446ac0536b81962f8628353afc7ccac8899801f32aee8a46b19b110ac","source":{"kind":"arxiv","id":"2601.05639","version":2},"attestation_state":"computed","paper":{"title":"Efficient training for compact compression models via sequential distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Caroline Mazini Rodrigues (COMPACT), Nicolas Keriven (COMPACT), Thomas Maugey (COMPACT)","submitted_at":"2026-01-09T08:50:38Z","abstract_excerpt":"Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high-quality reconstructions, they are typically complex, heavyweight, and require substantial training data and computational resources. We propose a methodology to significantly reduce autoencoder-based compression networks in a more stable Knowledge Distillation process. The intuition is that highly reduced architectures benefit from simplified optimization objectives in early training, with complexity gradually introduced later. Therefore, our appr"},"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":"2601.05639","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-09T08:50:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"10760300d174b26e17000642904f1450cd51f5856e6e49ba6eae40e6716178d0","abstract_canon_sha256":"a2cdfd77258006b90664a9ba7a44ed2a558b3d2ab607e14d7061cf2a8951c775"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:20.715513Z","signature_b64":"whNs4ZIUsUEUJEtirpr5bTGqAwNbXjfOXLp4iAwqAyL4mj1t/oJbALyoI/KrL492IPZlSCQRk89exXPGkiX9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4e57b3446ac0536b81962f8628353afc7ccac8899801f32aee8a46b19b110ac","last_reissued_at":"2026-05-21T01:04:20.714668Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:20.714668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient training for compact compression models via sequential distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Caroline Mazini Rodrigues (COMPACT), Nicolas Keriven (COMPACT), Thomas Maugey (COMPACT)","submitted_at":"2026-01-09T08:50:38Z","abstract_excerpt":"Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high-quality reconstructions, they are typically complex, heavyweight, and require substantial training data and computational resources. We propose a methodology to significantly reduce autoencoder-based compression networks in a more stable Knowledge Distillation process. The intuition is that highly reduced architectures benefit from simplified optimization objectives in early training, with complexity gradually introduced later. Therefore, our appr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.05639","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.05639/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":"2601.05639","created_at":"2026-05-21T01:04:20.714790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.05639v2","created_at":"2026-05-21T01:04:20.714790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.05639","created_at":"2026-05-21T01:04:20.714790+00:00"},{"alias_kind":"pith_short_12","alias_value":"6TSXWNCGVQCT","created_at":"2026-05-21T01:04:20.714790+00:00"},{"alias_kind":"pith_short_16","alias_value":"6TSXWNCGVQCTNOAZ","created_at":"2026-05-21T01:04:20.714790+00:00"},{"alias_kind":"pith_short_8","alias_value":"6TSXWNCG","created_at":"2026-05-21T01:04:20.714790+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2601.05639","citing_title":"Efficient training for compact compression models via sequential distillation","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7","json":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7.json","graph_json":"https://pith.science/api/pith-number/6TSXWNCGVQCTNOAZML4GFA2TV7/graph.json","events_json":"https://pith.science/api/pith-number/6TSXWNCGVQCTNOAZML4GFA2TV7/events.json","paper":"https://pith.science/paper/6TSXWNCG"},"agent_actions":{"view_html":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7","download_json":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7.json","view_paper":"https://pith.science/paper/6TSXWNCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.05639&json=true","fetch_graph":"https://pith.science/api/pith-number/6TSXWNCGVQCTNOAZML4GFA2TV7/graph.json","fetch_events":"https://pith.science/api/pith-number/6TSXWNCGVQCTNOAZML4GFA2TV7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7/action/storage_attestation","attest_author":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7/action/author_attestation","sign_citation":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7/action/citation_signature","submit_replication":"https://pith.science/pith/6TSXWNCGVQCTNOAZML4GFA2TV7/action/replication_record"}},"created_at":"2026-05-21T01:04:20.714790+00:00","updated_at":"2026-05-21T01:04:20.714790+00:00"}