{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:STDINY5C2L72CPVJPH5N4GCSHY","short_pith_number":"pith:STDINY5C","canonical_record":{"source":{"id":"1906.01819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-05T04:34:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5825df4c0c95cd3f7092e9ee0384f15e150153b1d961fd48e6eed8a5fae8368c","abstract_canon_sha256":"44ab8a2046beb2af9eac45db0cd1e0ca1804140b5ceeab74e8fd7f63de72be5e"},"schema_version":"1.0"},"canonical_sha256":"94c686e3a2d2ffa13ea979fade18523e33ae1450839a3e9897a3a2d80dd6c46a","source":{"kind":"arxiv","id":"1906.01819","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01819","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01819v1","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01819","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"STDINY5C2L72","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"STDINY5C2L72CPVJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"STDINY5C","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:STDINY5C2L72CPVJPH5N4GCSHY","target":"record","payload":{"canonical_record":{"source":{"id":"1906.01819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-05T04:34:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5825df4c0c95cd3f7092e9ee0384f15e150153b1d961fd48e6eed8a5fae8368c","abstract_canon_sha256":"44ab8a2046beb2af9eac45db0cd1e0ca1804140b5ceeab74e8fd7f63de72be5e"},"schema_version":"1.0"},"canonical_sha256":"94c686e3a2d2ffa13ea979fade18523e33ae1450839a3e9897a3a2d80dd6c46a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:06.139843Z","signature_b64":"IoOHbUDcAeVOW8fx1AdiZotgeBsI7y+U8d6QoJ+NF8YKNbcsFakJQjiUF2klX2Siab2lObPYyf1BuVmkBCHaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94c686e3a2d2ffa13ea979fade18523e33ae1450839a3e9897a3a2d80dd6c46a","last_reissued_at":"2026-05-17T23:44:06.139135Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:06.139135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.01819","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hfnWIVzwoVGND711K19Tf4HgMn2ZxXg0CefmXgMIqDM/6jHrOJDLDusintaTu6sNd0BtZx7q3smUekyMS7oMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T10:31:59.780276Z"},"content_sha256":"18949155fce345b774c5b2c830f4663c5fa0f3b84f346a796883c520cdcc6747","schema_version":"1.0","event_id":"sha256:18949155fce345b774c5b2c830f4663c5fa0f3b84f346a796883c520cdcc6747"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:STDINY5C2L72CPVJPH5N4GCSHY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Discriminative Few-Shot Learning Based on Directional Statistics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Dong-Yeon Cho, Jiwon Kim, Junyoung Park, Subin Yi, Yongseok Choi","submitted_at":"2019-06-05T04:34:10Z","abstract_excerpt":"Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorithm to generate class representatives for few-shot classification tasks. As a probabilistic model for learned features of inputs, we consider a mixture of von Mises-Fisher distributions which is known to be more expressive than Gaussian in a high dimensional space. Then, from a discriminative classifier perspective, we get a better class representative considering inter-class correlation which has not been addressed b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01819","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LhOY6l5nV27g0ZeYGGgTxNUI/i5yAfKfZyESPqMTitgZjAVqBMQdvaGJbxXOF8iBbnoTHoMHQ8oyE+/t+NTQCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T10:31:59.780639Z"},"content_sha256":"84de57281a36043a65dc08f5cf940a8f61f8e5e7be65c6745f006e034c22d31d","schema_version":"1.0","event_id":"sha256:84de57281a36043a65dc08f5cf940a8f61f8e5e7be65c6745f006e034c22d31d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/STDINY5C2L72CPVJPH5N4GCSHY/bundle.json","state_url":"https://pith.science/pith/STDINY5C2L72CPVJPH5N4GCSHY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/STDINY5C2L72CPVJPH5N4GCSHY/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":"2026-06-01T10:31:59Z","links":{"resolver":"https://pith.science/pith/STDINY5C2L72CPVJPH5N4GCSHY","bundle":"https://pith.science/pith/STDINY5C2L72CPVJPH5N4GCSHY/bundle.json","state":"https://pith.science/pith/STDINY5C2L72CPVJPH5N4GCSHY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/STDINY5C2L72CPVJPH5N4GCSHY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:STDINY5C2L72CPVJPH5N4GCSHY","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":"44ab8a2046beb2af9eac45db0cd1e0ca1804140b5ceeab74e8fd7f63de72be5e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-05T04:34:10Z","title_canon_sha256":"5825df4c0c95cd3f7092e9ee0384f15e150153b1d961fd48e6eed8a5fae8368c"},"schema_version":"1.0","source":{"id":"1906.01819","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01819","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01819v1","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01819","created_at":"2026-05-17T23:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"STDINY5C2L72","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"STDINY5C2L72CPVJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"STDINY5C","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:84de57281a36043a65dc08f5cf940a8f61f8e5e7be65c6745f006e034c22d31d","target":"graph","created_at":"2026-05-17T23:44:06Z","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":"Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorithm to generate class representatives for few-shot classification tasks. As a probabilistic model for learned features of inputs, we consider a mixture of von Mises-Fisher distributions which is known to be more expressive than Gaussian in a high dimensional space. Then, from a discriminative classifier perspective, we get a better class representative considering inter-class correlation which has not been addressed b","authors_text":"Dong-Yeon Cho, Jiwon Kim, Junyoung Park, Subin Yi, Yongseok Choi","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-05T04:34:10Z","title":"Discriminative Few-Shot Learning Based on Directional Statistics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01819","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":"sha256:18949155fce345b774c5b2c830f4663c5fa0f3b84f346a796883c520cdcc6747","target":"record","created_at":"2026-05-17T23:44:06Z","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":"44ab8a2046beb2af9eac45db0cd1e0ca1804140b5ceeab74e8fd7f63de72be5e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-05T04:34:10Z","title_canon_sha256":"5825df4c0c95cd3f7092e9ee0384f15e150153b1d961fd48e6eed8a5fae8368c"},"schema_version":"1.0","source":{"id":"1906.01819","kind":"arxiv","version":1}},"canonical_sha256":"94c686e3a2d2ffa13ea979fade18523e33ae1450839a3e9897a3a2d80dd6c46a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94c686e3a2d2ffa13ea979fade18523e33ae1450839a3e9897a3a2d80dd6c46a","first_computed_at":"2026-05-17T23:44:06.139135Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:06.139135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IoOHbUDcAeVOW8fx1AdiZotgeBsI7y+U8d6QoJ+NF8YKNbcsFakJQjiUF2klX2Siab2lObPYyf1BuVmkBCHaCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:06.139843Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.01819","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:18949155fce345b774c5b2c830f4663c5fa0f3b84f346a796883c520cdcc6747","sha256:84de57281a36043a65dc08f5cf940a8f61f8e5e7be65c6745f006e034c22d31d"],"state_sha256":"a0315bb441544b7981be55d37d09c68f5cdef6d59698bae01accc0fa1d828f5d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tlfHGNw/Z3txOik/oe3hfOsnLWPbZQXVjXhJLXPWt1iQ0kIhVVB9PH4FWLUgUqRdYXEcA0CB+X0kLdRzH+UvDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T10:31:59.782517Z","bundle_sha256":"f48c32ae9350cbecd321f97a46ca115f61597130b6dbdee508614ae193efe2e1"}}