{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UL73URDI4WZ5UIF4FA2G4QMRBY","short_pith_number":"pith:UL73URDI","canonical_record":{"source":{"id":"2009.13735","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T02:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"c4415dccd21070f27762589e49739bdf9d4681f305b07fad352bf47fcc7501ab","abstract_canon_sha256":"16f7ef68ff0c6a9a2f2731629fb171ee65b3ca94528abd527ded0091ecf1f356"},"schema_version":"1.0"},"canonical_sha256":"a2ffba4468e5b3da20bc28346e41910e0b207f704a8e0d7d9c2a67fffb3ca89a","source":{"kind":"arxiv","id":"2009.13735","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.13735","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"arxiv_version","alias_value":"2009.13735v2","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.13735","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_12","alias_value":"UL73URDI4WZ5","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_16","alias_value":"UL73URDI4WZ5UIF4","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_8","alias_value":"UL73URDI","created_at":"2026-07-05T01:41:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UL73URDI4WZ5UIF4FA2G4QMRBY","target":"record","payload":{"canonical_record":{"source":{"id":"2009.13735","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T02:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"c4415dccd21070f27762589e49739bdf9d4681f305b07fad352bf47fcc7501ab","abstract_canon_sha256":"16f7ef68ff0c6a9a2f2731629fb171ee65b3ca94528abd527ded0091ecf1f356"},"schema_version":"1.0"},"canonical_sha256":"a2ffba4468e5b3da20bc28346e41910e0b207f704a8e0d7d9c2a67fffb3ca89a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:57.597651Z","signature_b64":"XNRYoHGRuO26Ll/fsmy1yzj828XyxEZboi1P8pTMgOuEeyrUTsmSEca9J1RZGh00LwQ4foRWiWysBypDnrKhBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2ffba4468e5b3da20bc28346e41910e0b207f704a8e0d7d9c2a67fffb3ca89a","last_reissued_at":"2026-07-05T01:41:57.597299Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:57.597299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.13735","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:41:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vDtThLfvVoqxPjEKpKd6DZGhXNoof2U+1NVSiICmXsjqeEj72J1sm+L6LI0IaPDpIt5DT+5jpe9S3kx1PPJIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:10:25.561018Z"},"content_sha256":"53456d4a790ac4cf6b97b7b966439ebe6721307247e7669157007a091b33a7a1","schema_version":"1.0","event_id":"sha256:53456d4a790ac4cf6b97b7b966439ebe6721307247e7669157007a091b33a7a1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UL73URDI4WZ5UIF4FA2G4QMRBY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianping Wang, Qing Li, Tom Ko, Yangbin Chen, Yun Ma","submitted_at":"2020-09-29T02:44:13Z","abstract_excerpt":"Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new tasks. However, current MAML-based algorithms have limitations in forming generalizable decision boundaries. In this paper, we propose an approach called MetaMix. It generates virtual feature-target pairs within each episode to regularize the backbone models. MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.13735","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/2009.13735/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:41:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vcE1ZyNFZpbayoLVPsgoc6H4vSIAbGE8JG2e8bzsS7U1clZg7xMmJGmFD7U7bK1/jHyWDw6Jjes/2qqNZm+eCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:10:25.561399Z"},"content_sha256":"492aa62fbfc42dc01c2be7439b7dc5aa3a4d42852a8af55792256a4d22641571","schema_version":"1.0","event_id":"sha256:492aa62fbfc42dc01c2be7439b7dc5aa3a4d42852a8af55792256a4d22641571"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/bundle.json","state_url":"https://pith.science/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T19:10:25Z","links":{"resolver":"https://pith.science/pith/UL73URDI4WZ5UIF4FA2G4QMRBY","bundle":"https://pith.science/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/bundle.json","state":"https://pith.science/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UL73URDI4WZ5UIF4FA2G4QMRBY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UL73URDI4WZ5UIF4FA2G4QMRBY","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":"16f7ef68ff0c6a9a2f2731629fb171ee65b3ca94528abd527ded0091ecf1f356","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T02:44:13Z","title_canon_sha256":"c4415dccd21070f27762589e49739bdf9d4681f305b07fad352bf47fcc7501ab"},"schema_version":"1.0","source":{"id":"2009.13735","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.13735","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"arxiv_version","alias_value":"2009.13735v2","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.13735","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_12","alias_value":"UL73URDI4WZ5","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_16","alias_value":"UL73URDI4WZ5UIF4","created_at":"2026-07-05T01:41:57Z"},{"alias_kind":"pith_short_8","alias_value":"UL73URDI","created_at":"2026-07-05T01:41:57Z"}],"graph_snapshots":[{"event_id":"sha256:492aa62fbfc42dc01c2be7439b7dc5aa3a4d42852a8af55792256a4d22641571","target":"graph","created_at":"2026-07-05T01:41:57Z","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/2009.13735/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new tasks. However, current MAML-based algorithms have limitations in forming generalizable decision boundaries. In this paper, we propose an approach called MetaMix. It generates virtual feature-target pairs within each episode to regularize the backbone models. MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boun","authors_text":"Jianping Wang, Qing Li, Tom Ko, Yangbin Chen, Yun Ma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T02:44:13Z","title":"MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.13735","kind":"arxiv","version":2},"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":"sha256:53456d4a790ac4cf6b97b7b966439ebe6721307247e7669157007a091b33a7a1","target":"record","created_at":"2026-07-05T01:41:57Z","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":"16f7ef68ff0c6a9a2f2731629fb171ee65b3ca94528abd527ded0091ecf1f356","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-09-29T02:44:13Z","title_canon_sha256":"c4415dccd21070f27762589e49739bdf9d4681f305b07fad352bf47fcc7501ab"},"schema_version":"1.0","source":{"id":"2009.13735","kind":"arxiv","version":2}},"canonical_sha256":"a2ffba4468e5b3da20bc28346e41910e0b207f704a8e0d7d9c2a67fffb3ca89a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a2ffba4468e5b3da20bc28346e41910e0b207f704a8e0d7d9c2a67fffb3ca89a","first_computed_at":"2026-07-05T01:41:57.597299Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:41:57.597299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XNRYoHGRuO26Ll/fsmy1yzj828XyxEZboi1P8pTMgOuEeyrUTsmSEca9J1RZGh00LwQ4foRWiWysBypDnrKhBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:41:57.597651Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.13735","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53456d4a790ac4cf6b97b7b966439ebe6721307247e7669157007a091b33a7a1","sha256:492aa62fbfc42dc01c2be7439b7dc5aa3a4d42852a8af55792256a4d22641571"],"state_sha256":"4d2229a24e18d2a9e328c93921da79dbf6299afcfa17d9c1c62d10034bb8e08b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dx040fySfSAyeL2xa00zr7AtV917njs/cUIl8MuTH00z5xkwLDRSGzsJbP3ko9cBBvCdEHfxbO4BS45qjXaQAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:10:25.563288Z","bundle_sha256":"924e594414b76d8e2d62a9e110065d9121384f06004eb247dbd934e652106794"}}