{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TGVSCAN6JEPSPV5NHAFOMFXNUV","short_pith_number":"pith:TGVSCAN6","schema_version":"1.0","canonical_sha256":"99ab2101be491f27d7ad380ae616eda567c6f72170162fcfa6e7ad8a3ecc2fbe","source":{"kind":"arxiv","id":"1710.03463","version":1},"attestation_state":"computed","paper":{"title":"Learning to Generalize: Meta-Learning for Domain Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Da Li, Timothy M. Hospedales, Yi-Zhe Song, Yongxin Yang","submitted_at":"2017-10-10T09:15:16Z","abstract_excerpt":"Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We propose a novel {meta-learning} method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training "},"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":"1710.03463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-10T09:15:16Z","cross_cats_sorted":[],"title_canon_sha256":"59ff6872e5a907f48abc8022f5fe82ca19e55483de8a3579ed6f86e8f69913ae","abstract_canon_sha256":"762773246e7a97da633d0aca59ab9501c186c0cc3203f81f287a4a25a91f7675"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:11.850993Z","signature_b64":"+RuKsVMH55EPIMEnTDsg2DIgG4/r0ZgdpO58pmEoVijgDYKnDWJMs6ve2o/6oen+rGPe63bKBHwDKjoiLYloBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99ab2101be491f27d7ad380ae616eda567c6f72170162fcfa6e7ad8a3ecc2fbe","last_reissued_at":"2026-05-18T00:33:11.850484Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:11.850484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Generalize: Meta-Learning for Domain Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Da Li, Timothy M. Hospedales, Yi-Zhe Song, Yongxin Yang","submitted_at":"2017-10-10T09:15:16Z","abstract_excerpt":"Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. We propose a novel {meta-learning} method for domain generalization. Rather than designing a specific model that is robust to domain shift as in most previous DG work, we propose a model agnostic training procedure for DG. Our algorithm simulates train/test domain shift during training "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03463","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":"1710.03463","created_at":"2026-05-18T00:33:11.850561+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.03463v1","created_at":"2026-05-18T00:33:11.850561+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03463","created_at":"2026-05-18T00:33:11.850561+00:00"},{"alias_kind":"pith_short_12","alias_value":"TGVSCAN6JEPS","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TGVSCAN6JEPSPV5N","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TGVSCAN6","created_at":"2026-05-18T12:31:46.661854+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/TGVSCAN6JEPSPV5NHAFOMFXNUV","json":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV.json","graph_json":"https://pith.science/api/pith-number/TGVSCAN6JEPSPV5NHAFOMFXNUV/graph.json","events_json":"https://pith.science/api/pith-number/TGVSCAN6JEPSPV5NHAFOMFXNUV/events.json","paper":"https://pith.science/paper/TGVSCAN6"},"agent_actions":{"view_html":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV","download_json":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV.json","view_paper":"https://pith.science/paper/TGVSCAN6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.03463&json=true","fetch_graph":"https://pith.science/api/pith-number/TGVSCAN6JEPSPV5NHAFOMFXNUV/graph.json","fetch_events":"https://pith.science/api/pith-number/TGVSCAN6JEPSPV5NHAFOMFXNUV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV/action/storage_attestation","attest_author":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV/action/author_attestation","sign_citation":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV/action/citation_signature","submit_replication":"https://pith.science/pith/TGVSCAN6JEPSPV5NHAFOMFXNUV/action/replication_record"}},"created_at":"2026-05-18T00:33:11.850561+00:00","updated_at":"2026-05-18T00:33:11.850561+00:00"}