{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:FKVCRWJH6B33ZAZ2BTAGJZ55UT","short_pith_number":"pith:FKVCRWJH","canonical_record":{"source":{"id":"1903.05050","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-12T16:58:08Z","cross_cats_sorted":[],"title_canon_sha256":"ae9e4853a65936140cc12bb35728bf9d9e51bd84f97d8edc98ffaa2db2320fd3","abstract_canon_sha256":"d4a75dac1bc26f0109c67209c035fc2de8a4bb4110d816cd1edca9693c3f676f"},"schema_version":"1.0"},"canonical_sha256":"2aaa28d927f077bc833a0cc064e7bda4dd6b7efea0ba9dcc299ea81f65ab9057","source":{"kind":"arxiv","id":"1903.05050","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05050","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05050v1","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05050","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"FKVCRWJH6B33","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FKVCRWJH6B33ZAZ2","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FKVCRWJH","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:FKVCRWJH6B33ZAZ2BTAGJZ55UT","target":"record","payload":{"canonical_record":{"source":{"id":"1903.05050","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-12T16:58:08Z","cross_cats_sorted":[],"title_canon_sha256":"ae9e4853a65936140cc12bb35728bf9d9e51bd84f97d8edc98ffaa2db2320fd3","abstract_canon_sha256":"d4a75dac1bc26f0109c67209c035fc2de8a4bb4110d816cd1edca9693c3f676f"},"schema_version":"1.0"},"canonical_sha256":"2aaa28d927f077bc833a0cc064e7bda4dd6b7efea0ba9dcc299ea81f65ab9057","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:26.439825Z","signature_b64":"H81trxSKfLRm4RS6F9z6A4d4wu8Wze7utPSjNduEatmYlK65Kod36pu8XUCoWfctsMcbqW7EMYqpOe7x4oepDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2aaa28d927f077bc833a0cc064e7bda4dd6b7efea0ba9dcc299ea81f65ab9057","last_reissued_at":"2026-05-17T23:51:26.439311Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:26.439311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.05050","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":"2026-05-17T23:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pehNmGkV5zSqqgeBfdEKMpzrO4gETakojGjb9Hy1K4srAHfVYJXYIFRuLlEpvGsX5F0XcGqyUpX8AkQV4WuVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T22:45:46.330238Z"},"content_sha256":"e4acae2ed7521a56c1a77ba39133e1295a6bc70484010c80770905eeaf459af5","schema_version":"1.0","event_id":"sha256:e4acae2ed7521a56c1a77ba39133e1295a6bc70484010c80770905eeaf459af5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:FKVCRWJH6B33ZAZ2BTAGJZ55UT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dense Classification and Implanting for Few-Shot Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrei Bursuc, Sylvaine Picard, Yannis Avrithis, Yann Lifchitz","submitted_at":"2019-03-12T16:58:08Z","abstract_excerpt":"Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05050","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":"2026-05-17T23:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kca1jSb4hwGzdHp+0YCpE0/5DFpbZWvKWKXRA2VntM/3Kdvw/IxnaO8N6mwwjI9CAnkCSWThLmbw3WCDkHX8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T22:45:46.330608Z"},"content_sha256":"27fb539e48c0a2b1282a40429c779bf82ef6c312d070b83588ef84f64da576c8","schema_version":"1.0","event_id":"sha256:27fb539e48c0a2b1282a40429c779bf82ef6c312d070b83588ef84f64da576c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/bundle.json","state_url":"https://pith.science/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/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":"2026-06-24T22:45:46Z","links":{"resolver":"https://pith.science/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT","bundle":"https://pith.science/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/bundle.json","state":"https://pith.science/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FKVCRWJH6B33ZAZ2BTAGJZ55UT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FKVCRWJH6B33ZAZ2BTAGJZ55UT","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":"d4a75dac1bc26f0109c67209c035fc2de8a4bb4110d816cd1edca9693c3f676f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-12T16:58:08Z","title_canon_sha256":"ae9e4853a65936140cc12bb35728bf9d9e51bd84f97d8edc98ffaa2db2320fd3"},"schema_version":"1.0","source":{"id":"1903.05050","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.05050","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"1903.05050v1","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05050","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"FKVCRWJH6B33","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FKVCRWJH6B33ZAZ2","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FKVCRWJH","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:27fb539e48c0a2b1282a40429c779bf82ef6c312d070b83588ef84f64da576c8","target":"graph","created_at":"2026-05-17T23:51:26Z","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":"Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art","authors_text":"Andrei Bursuc, Sylvaine Picard, Yannis Avrithis, Yann Lifchitz","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-12T16:58:08Z","title":"Dense Classification and Implanting for Few-Shot Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05050","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:e4acae2ed7521a56c1a77ba39133e1295a6bc70484010c80770905eeaf459af5","target":"record","created_at":"2026-05-17T23:51:26Z","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":"d4a75dac1bc26f0109c67209c035fc2de8a4bb4110d816cd1edca9693c3f676f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-12T16:58:08Z","title_canon_sha256":"ae9e4853a65936140cc12bb35728bf9d9e51bd84f97d8edc98ffaa2db2320fd3"},"schema_version":"1.0","source":{"id":"1903.05050","kind":"arxiv","version":1}},"canonical_sha256":"2aaa28d927f077bc833a0cc064e7bda4dd6b7efea0ba9dcc299ea81f65ab9057","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2aaa28d927f077bc833a0cc064e7bda4dd6b7efea0ba9dcc299ea81f65ab9057","first_computed_at":"2026-05-17T23:51:26.439311Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:26.439311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"H81trxSKfLRm4RS6F9z6A4d4wu8Wze7utPSjNduEatmYlK65Kod36pu8XUCoWfctsMcbqW7EMYqpOe7x4oepDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:26.439825Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.05050","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4acae2ed7521a56c1a77ba39133e1295a6bc70484010c80770905eeaf459af5","sha256:27fb539e48c0a2b1282a40429c779bf82ef6c312d070b83588ef84f64da576c8"],"state_sha256":"3a5ddf03935859f9f2274fb2b48a14cbb5f2a7ef6a7bfe418320d614015efb18"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5ySwsO0FpsOcpdu49v88Ch4OJ++jic1rZ4fpcEsuzqvDcqyfPPv+R0eK3pfrvh/3qNsnOprU3zhK/iuL+2dVAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T22:45:46.332519Z","bundle_sha256":"0f0d266e5bac2e7afa27fe85bbb3199e26c63e15874bd2431b820e33c2e15983"}}