{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FSH4BXI3MXDUIYDNSAK2KG3VT6","short_pith_number":"pith:FSH4BXI3","schema_version":"1.0","canonical_sha256":"2c8fc0dd1b65c744606d9015a51b759f990f9474b7f6a55ee5edd5ad868cedd3","source":{"kind":"arxiv","id":"2204.08040","version":2},"attestation_state":"computed","paper":{"title":"NICO++: Towards Better Benchmarking for Domain Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Han Yu, Peng Cui, Renzhe Xu, Xingxuan Zhang, Yue He, Zheyan Shen","submitted_at":"2022-04-17T15:57:12Z","abstract_excerpt":"Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respecti"},"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":"2204.08040","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-17T15:57:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2ca81071b7918269166a725cd155306ecdeccef53add7263a16091ae65f91daf","abstract_canon_sha256":"671291a918ec95b64f15abe8bfc018d9d78bee3e1fd931e2bac78b3338649eb0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:41.364271Z","signature_b64":"2WR5yrQX2gbSbL7EC2+36MakAP5wIip6tsbLIrvJNEj5ljjcEZo7lNiazTh1rr6b4JjpxRS+ZLaxJXAe7hnbAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c8fc0dd1b65c744606d9015a51b759f990f9474b7f6a55ee5edd5ad868cedd3","last_reissued_at":"2026-07-05T04:16:41.363858Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:41.363858Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NICO++: Towards Better Benchmarking for Domain Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Han Yu, Peng Cui, Renzhe Xu, Xingxuan Zhang, Yue He, Zheyan Shen","submitted_at":"2022-04-17T15:57:12Z","abstract_excerpt":"Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08040","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/2204.08040/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":"2204.08040","created_at":"2026-07-05T04:16:41.363919+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.08040v2","created_at":"2026-07-05T04:16:41.363919+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08040","created_at":"2026-07-05T04:16:41.363919+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSH4BXI3MXDU","created_at":"2026-07-05T04:16:41.363919+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSH4BXI3MXDUIYDN","created_at":"2026-07-05T04:16:41.363919+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSH4BXI3","created_at":"2026-07-05T04:16:41.363919+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/FSH4BXI3MXDUIYDNSAK2KG3VT6","json":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6.json","graph_json":"https://pith.science/api/pith-number/FSH4BXI3MXDUIYDNSAK2KG3VT6/graph.json","events_json":"https://pith.science/api/pith-number/FSH4BXI3MXDUIYDNSAK2KG3VT6/events.json","paper":"https://pith.science/paper/FSH4BXI3"},"agent_actions":{"view_html":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6","download_json":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6.json","view_paper":"https://pith.science/paper/FSH4BXI3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.08040&json=true","fetch_graph":"https://pith.science/api/pith-number/FSH4BXI3MXDUIYDNSAK2KG3VT6/graph.json","fetch_events":"https://pith.science/api/pith-number/FSH4BXI3MXDUIYDNSAK2KG3VT6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6/action/storage_attestation","attest_author":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6/action/author_attestation","sign_citation":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6/action/citation_signature","submit_replication":"https://pith.science/pith/FSH4BXI3MXDUIYDNSAK2KG3VT6/action/replication_record"}},"created_at":"2026-07-05T04:16:41.363919+00:00","updated_at":"2026-07-05T04:16:41.363919+00:00"}