{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:NQKYYSQMNP6Y5SRVYAQ43QLYJM","short_pith_number":"pith:NQKYYSQM","canonical_record":{"source":{"id":"2110.03909","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-08T06:07:21Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3e2955c85d093164314b9fdd68cfa7fc634af2cd7997c145d8ee9400d180035b","abstract_canon_sha256":"7f7df246552e9d3cd53293ba67f25cf63df1443654c9c4bacdab7437e8e9bc8b"},"schema_version":"1.0"},"canonical_sha256":"6c158c4a0c6bfd8eca35c021cdc1784b1f621705f92baf9280b0d0493079b7f2","source":{"kind":"arxiv","id":"2110.03909","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.03909","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"arxiv_version","alias_value":"2110.03909v2","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03909","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_12","alias_value":"NQKYYSQMNP6Y","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_16","alias_value":"NQKYYSQMNP6Y5SRV","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_8","alias_value":"NQKYYSQM","created_at":"2026-07-05T03:23:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:NQKYYSQMNP6Y5SRVYAQ43QLYJM","target":"record","payload":{"canonical_record":{"source":{"id":"2110.03909","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-08T06:07:21Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3e2955c85d093164314b9fdd68cfa7fc634af2cd7997c145d8ee9400d180035b","abstract_canon_sha256":"7f7df246552e9d3cd53293ba67f25cf63df1443654c9c4bacdab7437e8e9bc8b"},"schema_version":"1.0"},"canonical_sha256":"6c158c4a0c6bfd8eca35c021cdc1784b1f621705f92baf9280b0d0493079b7f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:15.107397Z","signature_b64":"P8pxfwF2TowtKT7cGsnecl/cIs/rORCTqjrvn0Ciduo8bsvvCBQLDb5T97Df5OMo/6NFbJaNsKbAFPBTbiBYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c158c4a0c6bfd8eca35c021cdc1784b1f621705f92baf9280b0d0493079b7f2","last_reissued_at":"2026-07-05T03:23:15.106960Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:15.106960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.03909","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":"2026-07-05T03:23:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"elUrQqfWznIabbuUpmOAOouwCe/Ioj1K/tuqqui8alBMIAmwDFILA1N72yZ2Ma2sv43samesZ5XlaH2L3/iNAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:47:32.837474Z"},"content_sha256":"042fc82690ccfa89df587053e2939ba6ac42277c9a55c0cc70efe38066b64c7a","schema_version":"1.0","event_id":"sha256:042fc82690ccfa89df587053e2939ba6ac42277c9a55c0cc70efe38066b64c7a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:NQKYYSQMNP6Y5SRVYAQ43QLYJM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Dohee Cho, Heewon Kim, Jaesik Min, Janghoon Choi, Kyoung Mu Lee, Sungyong Baik","submitted_at":"2021-10-08T06:07:21Z","abstract_excerpt":"In few-shot learning scenarios, the challenge is to generalize and perform well on new unseen examples when only very few labeled examples are available for each task. Model-agnostic meta-learning (MAML) has gained the popularity as one of the representative few-shot learning methods for its flexibility and applicability to diverse problems. However, MAML and its variants often resort to a simple loss function without any auxiliary loss function or regularization terms that can help achieve better generalization. The problem lies in that each application and task may require different auxiliar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03909","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/2110.03909/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":"2026-07-05T03:23:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"huuu+/yas1jYEjNQMt724TW7uexMAZM7b8gr3CD5C4kqKiBt+9GYICMfIc9JxSICeUdzQ+epsbjG07IjqnvvCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:47:32.837843Z"},"content_sha256":"a3b178183defbdc6fd066e053631dcc0ecdaa86dd84661f3c4f57237320a1ffd","schema_version":"1.0","event_id":"sha256:a3b178183defbdc6fd066e053631dcc0ecdaa86dd84661f3c4f57237320a1ffd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/bundle.json","state_url":"https://pith.science/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/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":"2026-07-06T18:47:32Z","links":{"resolver":"https://pith.science/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM","bundle":"https://pith.science/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/bundle.json","state":"https://pith.science/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NQKYYSQMNP6Y5SRVYAQ43QLYJM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:NQKYYSQMNP6Y5SRVYAQ43QLYJM","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":"7f7df246552e9d3cd53293ba67f25cf63df1443654c9c4bacdab7437e8e9bc8b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-08T06:07:21Z","title_canon_sha256":"3e2955c85d093164314b9fdd68cfa7fc634af2cd7997c145d8ee9400d180035b"},"schema_version":"1.0","source":{"id":"2110.03909","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.03909","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"arxiv_version","alias_value":"2110.03909v2","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03909","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_12","alias_value":"NQKYYSQMNP6Y","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_16","alias_value":"NQKYYSQMNP6Y5SRV","created_at":"2026-07-05T03:23:15Z"},{"alias_kind":"pith_short_8","alias_value":"NQKYYSQM","created_at":"2026-07-05T03:23:15Z"}],"graph_snapshots":[{"event_id":"sha256:a3b178183defbdc6fd066e053631dcc0ecdaa86dd84661f3c4f57237320a1ffd","target":"graph","created_at":"2026-07-05T03:23:15Z","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/2110.03909/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In few-shot learning scenarios, the challenge is to generalize and perform well on new unseen examples when only very few labeled examples are available for each task. Model-agnostic meta-learning (MAML) has gained the popularity as one of the representative few-shot learning methods for its flexibility and applicability to diverse problems. However, MAML and its variants often resort to a simple loss function without any auxiliary loss function or regularization terms that can help achieve better generalization. The problem lies in that each application and task may require different auxiliar","authors_text":"Dohee Cho, Heewon Kim, Jaesik Min, Janghoon Choi, Kyoung Mu Lee, Sungyong Baik","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-08T06:07:21Z","title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03909","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":"sha256:042fc82690ccfa89df587053e2939ba6ac42277c9a55c0cc70efe38066b64c7a","target":"record","created_at":"2026-07-05T03:23:15Z","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":"7f7df246552e9d3cd53293ba67f25cf63df1443654c9c4bacdab7437e8e9bc8b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-08T06:07:21Z","title_canon_sha256":"3e2955c85d093164314b9fdd68cfa7fc634af2cd7997c145d8ee9400d180035b"},"schema_version":"1.0","source":{"id":"2110.03909","kind":"arxiv","version":2}},"canonical_sha256":"6c158c4a0c6bfd8eca35c021cdc1784b1f621705f92baf9280b0d0493079b7f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c158c4a0c6bfd8eca35c021cdc1784b1f621705f92baf9280b0d0493079b7f2","first_computed_at":"2026-07-05T03:23:15.106960Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:23:15.106960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P8pxfwF2TowtKT7cGsnecl/cIs/rORCTqjrvn0Ciduo8bsvvCBQLDb5T97Df5OMo/6NFbJaNsKbAFPBTbiBYCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:23:15.107397Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.03909","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:042fc82690ccfa89df587053e2939ba6ac42277c9a55c0cc70efe38066b64c7a","sha256:a3b178183defbdc6fd066e053631dcc0ecdaa86dd84661f3c4f57237320a1ffd"],"state_sha256":"751c6ba8cfea2b2515b4e448987b4f51f32b33145e7a891759ee178bd6b115ab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a0Uh7lloROByfIR/Y8AeP486LKxLNU+Kbl/M9k6Nz3gibpvBc1bQjc4d8rcouYklUB6l3El8WyUX8kSgDBHeCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:47:32.839804Z","bundle_sha256":"dbffec3bc4d73c195b26983b053bebeb59ece172858015ab045a73178320d457"}}