{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:IIO63B4EG7RY5UOHE46TMWP2RY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"760681ff75cfa05f4cd0d33d853930ab68834479e492e79d55961309945a4be0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-14T20:48:12Z","title_canon_sha256":"3cb7ea7113216b61f4f1d72dd71f9a1b16c50d3fa092349b708faa0c8ff6bf44"},"schema_version":"1.0","source":{"id":"2210.08101","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08101","created_at":"2026-07-05T06:51:20Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08101v3","created_at":"2026-07-05T06:51:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08101","created_at":"2026-07-05T06:51:20Z"},{"alias_kind":"pith_short_12","alias_value":"IIO63B4EG7RY","created_at":"2026-07-05T06:51:20Z"},{"alias_kind":"pith_short_16","alias_value":"IIO63B4EG7RY5UOH","created_at":"2026-07-05T06:51:20Z"},{"alias_kind":"pith_short_8","alias_value":"IIO63B4E","created_at":"2026-07-05T06:51:20Z"}],"graph_snapshots":[{"event_id":"sha256:ab85601d1e0fb7dfcbcdd3c0fba5a52f84de2d6b1d898847e224b25e66965d5c","target":"graph","created_at":"2026-07-05T06:51:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.08101/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant amount of parameters. Such requirements hinder the use in resource-limited environments and demand both software and hardware optimization. Another limitation is that deep models are usually specialized into a single domain or task, requiring them to learn and store new parameters for each new one. Multi-Domain Learning (MDL) attempts to solve this problem by learning a single model that is capable of performing wel","authors_text":"Jurandy Almeida, Nicu Sebe, Rodrigo Berriel, Samuel Felipe dos Santos, Thiago Oliveira-Santos","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-14T20:48:12Z","title":"Budget-Aware Pruning for Multi-Domain Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08101","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f90840c4e61e74cc1afc21624717e5ef8b4863a6d01f6df8b019703f5e336869","target":"record","created_at":"2026-07-05T06:51:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"760681ff75cfa05f4cd0d33d853930ab68834479e492e79d55961309945a4be0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-14T20:48:12Z","title_canon_sha256":"3cb7ea7113216b61f4f1d72dd71f9a1b16c50d3fa092349b708faa0c8ff6bf44"},"schema_version":"1.0","source":{"id":"2210.08101","kind":"arxiv","version":3}},"canonical_sha256":"421ded878437e38ed1c7273d3659fa8e02d6500174ed9a1b4bebd821f8af3222","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"421ded878437e38ed1c7273d3659fa8e02d6500174ed9a1b4bebd821f8af3222","first_computed_at":"2026-07-05T06:51:20.101328Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:51:20.101328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WqIDhT+YrZQ5YhMHL409BtWArAz9itJzeaBK63r3ylIegng02f1ol03Zu95OhbjIUzK9vCZ+RV5KgrP0/NaPBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:51:20.101844Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.08101","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f90840c4e61e74cc1afc21624717e5ef8b4863a6d01f6df8b019703f5e336869","sha256:ab85601d1e0fb7dfcbcdd3c0fba5a52f84de2d6b1d898847e224b25e66965d5c"],"state_sha256":"9b63645249e3636f2d056fcc66d38999483070d293799d2dc16c4568fc3b684b"}