{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PFSK3F227CCMOYXAZIVDH3TKJO","short_pith_number":"pith:PFSK3F22","schema_version":"1.0","canonical_sha256":"7964ad975af884c762e0ca2a33ee6a4bab8b257478cd97c243c3eb55711e99f2","source":{"kind":"arxiv","id":"1808.07270","version":1},"attestation_state":"computed","paper":{"title":"Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinchao Liu, Margarita Osadchy, Stuart J. Gibson","submitted_at":"2018-08-22T08:29:16Z","abstract_excerpt":"Deep Learning shows very good performance when trained on large labeled data sets. The problem of training a deep net on a few or one sample per class requires a different learning approach which can generalize to unseen classes using only a few representatives of these classes. This problem has previously been approached by meta-learning. Here we propose a novel meta-learner which shows state-of-the-art performance on common benchmarks for one/few shot classification. Our model features three novel components: First is a feed-forward embedding that takes random class support samples (after a "},"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":"1808.07270","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-22T08:29:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"26c3247b2731a2500b81c50bffb061059752c356499dec7abfbcf34ad40760cb","abstract_canon_sha256":"eb01209d74b8e88701dca6b47640690b2b1619d04815df62bb7f6ac6f7bedcc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:29.376936Z","signature_b64":"JP+TEVA7tsRb4FdwRu6DtBWX3xm6YYf5PsC+0EEBq8rcyy/3cDWBAafqb8I9hlJkLQRK5r6vFfiwnrORCCqaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7964ad975af884c762e0ca2a33ee6a4bab8b257478cd97c243c3eb55711e99f2","last_reissued_at":"2026-05-18T00:07:29.376451Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:29.376451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinchao Liu, Margarita Osadchy, Stuart J. Gibson","submitted_at":"2018-08-22T08:29:16Z","abstract_excerpt":"Deep Learning shows very good performance when trained on large labeled data sets. The problem of training a deep net on a few or one sample per class requires a different learning approach which can generalize to unseen classes using only a few representatives of these classes. This problem has previously been approached by meta-learning. Here we propose a novel meta-learner which shows state-of-the-art performance on common benchmarks for one/few shot classification. Our model features three novel components: First is a feed-forward embedding that takes random class support samples (after a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07270","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":"1808.07270","created_at":"2026-05-18T00:07:29.376523+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07270v1","created_at":"2026-05-18T00:07:29.376523+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07270","created_at":"2026-05-18T00:07:29.376523+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFSK3F227CCM","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFSK3F227CCMOYXA","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFSK3F22","created_at":"2026-05-18T12:32:43.782077+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/PFSK3F227CCMOYXAZIVDH3TKJO","json":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO.json","graph_json":"https://pith.science/api/pith-number/PFSK3F227CCMOYXAZIVDH3TKJO/graph.json","events_json":"https://pith.science/api/pith-number/PFSK3F227CCMOYXAZIVDH3TKJO/events.json","paper":"https://pith.science/paper/PFSK3F22"},"agent_actions":{"view_html":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO","download_json":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO.json","view_paper":"https://pith.science/paper/PFSK3F22","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07270&json=true","fetch_graph":"https://pith.science/api/pith-number/PFSK3F227CCMOYXAZIVDH3TKJO/graph.json","fetch_events":"https://pith.science/api/pith-number/PFSK3F227CCMOYXAZIVDH3TKJO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO/action/storage_attestation","attest_author":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO/action/author_attestation","sign_citation":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO/action/citation_signature","submit_replication":"https://pith.science/pith/PFSK3F227CCMOYXAZIVDH3TKJO/action/replication_record"}},"created_at":"2026-05-18T00:07:29.376523+00:00","updated_at":"2026-05-18T00:07:29.376523+00:00"}