{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:G5DA2ZHSX26U5QINZEZFIRL55F","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":"cb6416c30f116f11eed93ab4394b23c282ce049f2a661720d2f4945b1bd8b737","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-23T20:17:59Z","title_canon_sha256":"8a67e27e46c72116e7d5749525d825f988a987fc8e6fe5da729bb2e70212695a"},"schema_version":"1.0","source":{"id":"1805.10123","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10123","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10123v4","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10123","created_at":"2026-05-17T23:55:34Z"},{"alias_kind":"pith_short_12","alias_value":"G5DA2ZHSX26U","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"G5DA2ZHSX26U5QIN","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"G5DA2ZHS","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:a22334243069f1d9f118bbf6e8c6ef2e3626a5ebd0bee5082f5ea830cf3a6a43","target":"graph","created_at":"2026-05-17T23:55:34Z","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"},"paper":{"abstract_excerpt":"Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in lea","authors_text":"Alexandre Lacoste, Boris N. Oreshkin, Pau Rodriguez","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-23T20:17:59Z","title":"TADAM: Task dependent adaptive metric for improved few-shot learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10123","kind":"arxiv","version":4},"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":"sha256:821ee6a5d5432dc1ad9fb48f654c0ee9e93161f951929458690991539dc95393","target":"record","created_at":"2026-05-17T23:55:34Z","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":"cb6416c30f116f11eed93ab4394b23c282ce049f2a661720d2f4945b1bd8b737","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-23T20:17:59Z","title_canon_sha256":"8a67e27e46c72116e7d5749525d825f988a987fc8e6fe5da729bb2e70212695a"},"schema_version":"1.0","source":{"id":"1805.10123","kind":"arxiv","version":4}},"canonical_sha256":"37460d64f2bebd4ec10dc93254457de9427ed3732a7c2b2c2474b9ed15bb175d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37460d64f2bebd4ec10dc93254457de9427ed3732a7c2b2c2474b9ed15bb175d","first_computed_at":"2026-05-17T23:55:34.427588Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:34.427588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mPoZKUsXBbQf7qMRhr9ECrK+pA6UCYm9febcqAPOLzw+HG4fxTZjojlpfjQmLr4KSb7Dv++y/X7g1haPXvgaAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:34.428041Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10123","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:821ee6a5d5432dc1ad9fb48f654c0ee9e93161f951929458690991539dc95393","sha256:a22334243069f1d9f118bbf6e8c6ef2e3626a5ebd0bee5082f5ea830cf3a6a43"],"state_sha256":"f88defe2f0b72d95d32784ab0962694ecb3a140911804396f5f3ae19d4c7126d"}