{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FGPV2PUMSQKXEPLU5SBZI5BH5S","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":"0ae4463efe5f81210ccb0fc902f807646c93a0d32a1384490ccded6230c8eeda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T15:56:55Z","title_canon_sha256":"ab5502c492ec65441fca0a4bf7249b80465a346ed648901c7b998ebee0f9fc38"},"schema_version":"1.0","source":{"id":"2501.14593","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14593","created_at":"2026-07-05T10:05:03Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14593v1","created_at":"2026-07-05T10:05:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14593","created_at":"2026-07-05T10:05:03Z"},{"alias_kind":"pith_short_12","alias_value":"FGPV2PUMSQKX","created_at":"2026-07-05T10:05:03Z"},{"alias_kind":"pith_short_16","alias_value":"FGPV2PUMSQKXEPLU","created_at":"2026-07-05T10:05:03Z"},{"alias_kind":"pith_short_8","alias_value":"FGPV2PUM","created_at":"2026-07-05T10:05:03Z"}],"graph_snapshots":[{"event_id":"sha256:12ad996e6bd1ca49b2bae40b44b36f2fdfa943ef1e708fc78c1d5a86a5f2eb95","target":"graph","created_at":"2026-07-05T10:05:03Z","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/2501.14593/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Few-shot learning (FSL) is a challenging task in machine learning, demanding a model to render discriminative classification by using only a few labeled samples. In the literature of FSL, deep models are trained in a manner of metric learning to provide metric in a feature space which is well generalizable to classify samples of novel classes; in the space, even a few amount of labeled training examples can construct an effective classifier. In this paper, we propose a novel FSL loss based on \\emph{geometric mean} to embed discriminative metric into deep features. In contrast to the other loss","authors_text":"Takumi Kobayashi, Tong Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T15:56:55Z","title":"Geometric Mean Improves Loss For Few-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14593","kind":"arxiv","version":1},"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":"sha256:99565f0d508d76db55c1a7571d66606a9ff14dfc996f72fafef31d24c2810d17","target":"record","created_at":"2026-07-05T10:05:03Z","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":"0ae4463efe5f81210ccb0fc902f807646c93a0d32a1384490ccded6230c8eeda","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T15:56:55Z","title_canon_sha256":"ab5502c492ec65441fca0a4bf7249b80465a346ed648901c7b998ebee0f9fc38"},"schema_version":"1.0","source":{"id":"2501.14593","kind":"arxiv","version":1}},"canonical_sha256":"299f5d3e8c9415723d74ec83947427ecb625235d337bb0b0562c283dac8c1aa3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"299f5d3e8c9415723d74ec83947427ecb625235d337bb0b0562c283dac8c1aa3","first_computed_at":"2026-07-05T10:05:03.060784Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:03.060784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"25h6vhyyU8Ze73V30p2WufLp7H1F5ba8/RNI96ndS8zfCBV36aARARFalc3ssftVFGESTTh5tydj0ISV7io1AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:03.061116Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14593","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99565f0d508d76db55c1a7571d66606a9ff14dfc996f72fafef31d24c2810d17","sha256:12ad996e6bd1ca49b2bae40b44b36f2fdfa943ef1e708fc78c1d5a86a5f2eb95"],"state_sha256":"61a4148a79d199b255c8a5c436fd1f35853d2550cc440d86fc0ba22d1e0036a0"}