{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:XYVLNHGU7CCDENWGUNL2OBHD6R","short_pith_number":"pith:XYVLNHGU","canonical_record":{"source":{"id":"1903.11176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-26T22:05:58Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d2d2d1c7977ec7ca512e8c7836fb4f3c05db67bfefbea6a85fa1bc79a857c8c1","abstract_canon_sha256":"a38b505be12ed78c31cd8c4e21b0f452d18efd5c83ae29626a6ebbca58bee8bb"},"schema_version":"1.0"},"canonical_sha256":"be2ab69cd4f8843236c6a357a704e3f477efa0ec7f111a6984c35d33615169fb","source":{"kind":"arxiv","id":"1903.11176","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11176","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11176v1","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11176","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"pith_short_12","alias_value":"XYVLNHGU7CCD","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XYVLNHGU7CCDENWG","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XYVLNHGU","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:XYVLNHGU7CCDENWGUNL2OBHD6R","target":"record","payload":{"canonical_record":{"source":{"id":"1903.11176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-26T22:05:58Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d2d2d1c7977ec7ca512e8c7836fb4f3c05db67bfefbea6a85fa1bc79a857c8c1","abstract_canon_sha256":"a38b505be12ed78c31cd8c4e21b0f452d18efd5c83ae29626a6ebbca58bee8bb"},"schema_version":"1.0"},"canonical_sha256":"be2ab69cd4f8843236c6a357a704e3f477efa0ec7f111a6984c35d33615169fb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:03.957848Z","signature_b64":"FfaaQWpIxbo4aIrfK9g2/ggmB2osndZZMRQEyeJWBaJ+xhSWgHwjHd0w3KqdMApKXiOihqttVE1gpD+dBtJsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be2ab69cd4f8843236c6a357a704e3f477efa0ec7f111a6984c35d33615169fb","last_reissued_at":"2026-05-17T23:50:03.957432Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:03.957432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.11176","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":"2026-05-17T23:50:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yiK6Ay3EiazJ0J5MQags/DuE3NiuNw6Bv2v+wtP8W7SCtefTPksbfFTRERGygjffFiwmzwQhtOmY7JrCHjwiAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:27:29.139854Z"},"content_sha256":"94a5f65a7700eb124df693a13ad10b42879f715514b129c45bb5f41624f12db2","schema_version":"1.0","event_id":"sha256:94a5f65a7700eb124df693a13ad10b42879f715514b129c45bb5f41624f12db2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:XYVLNHGU7CCDENWGUNL2OBHD6R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On evaluating CNN representations for low resource medical image classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Rahul Gupta, Shrikanth Narayanan, Taruna Agrawal","submitted_at":"2019-03-26T22:05:58Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have revolutionized performances in several machine learning tasks such as image classification, object tracking, and keyword spotting. However, given that they contain a large number of parameters, their direct applicability into low resource tasks is not straightforward. In this work, we experiment with an application of CNN models to gastrointestinal landmark classification with only a few thousands of training samples through transfer learning. As in a standard transfer learning approach, we train CNNs on a large external corpus, followed by representat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11176","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":"2026-05-17T23:50:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9vOD4tXUni55WHOVCF5BB4Qy/gRsLB+yUm9qUgxufwhI79swIjuEkmZQ8+jGRtztLZSmruRD3Ve1INFaNvpAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:27:29.140563Z"},"content_sha256":"31866e4753949b5809d005f2d3e68a3d327de0c0a169d10308fc68bf517e2e84","schema_version":"1.0","event_id":"sha256:31866e4753949b5809d005f2d3e68a3d327de0c0a169d10308fc68bf517e2e84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/bundle.json","state_url":"https://pith.science/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/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":"2026-06-07T17:27:29Z","links":{"resolver":"https://pith.science/pith/XYVLNHGU7CCDENWGUNL2OBHD6R","bundle":"https://pith.science/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/bundle.json","state":"https://pith.science/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XYVLNHGU7CCDENWGUNL2OBHD6R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:XYVLNHGU7CCDENWGUNL2OBHD6R","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":"a38b505be12ed78c31cd8c4e21b0f452d18efd5c83ae29626a6ebbca58bee8bb","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-26T22:05:58Z","title_canon_sha256":"d2d2d1c7977ec7ca512e8c7836fb4f3c05db67bfefbea6a85fa1bc79a857c8c1"},"schema_version":"1.0","source":{"id":"1903.11176","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11176","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11176v1","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11176","created_at":"2026-05-17T23:50:03Z"},{"alias_kind":"pith_short_12","alias_value":"XYVLNHGU7CCD","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XYVLNHGU7CCDENWG","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XYVLNHGU","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:31866e4753949b5809d005f2d3e68a3d327de0c0a169d10308fc68bf517e2e84","target":"graph","created_at":"2026-05-17T23:50:03Z","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"},"paper":{"abstract_excerpt":"Convolutional Neural Networks (CNNs) have revolutionized performances in several machine learning tasks such as image classification, object tracking, and keyword spotting. However, given that they contain a large number of parameters, their direct applicability into low resource tasks is not straightforward. In this work, we experiment with an application of CNN models to gastrointestinal landmark classification with only a few thousands of training samples through transfer learning. As in a standard transfer learning approach, we train CNNs on a large external corpus, followed by representat","authors_text":"Rahul Gupta, Shrikanth Narayanan, Taruna Agrawal","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-26T22:05:58Z","title":"On evaluating CNN representations for low resource medical image classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11176","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":"sha256:94a5f65a7700eb124df693a13ad10b42879f715514b129c45bb5f41624f12db2","target":"record","created_at":"2026-05-17T23:50:03Z","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":"a38b505be12ed78c31cd8c4e21b0f452d18efd5c83ae29626a6ebbca58bee8bb","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-03-26T22:05:58Z","title_canon_sha256":"d2d2d1c7977ec7ca512e8c7836fb4f3c05db67bfefbea6a85fa1bc79a857c8c1"},"schema_version":"1.0","source":{"id":"1903.11176","kind":"arxiv","version":1}},"canonical_sha256":"be2ab69cd4f8843236c6a357a704e3f477efa0ec7f111a6984c35d33615169fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be2ab69cd4f8843236c6a357a704e3f477efa0ec7f111a6984c35d33615169fb","first_computed_at":"2026-05-17T23:50:03.957432Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:03.957432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FfaaQWpIxbo4aIrfK9g2/ggmB2osndZZMRQEyeJWBaJ+xhSWgHwjHd0w3KqdMApKXiOihqttVE1gpD+dBtJsDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:03.957848Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.11176","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94a5f65a7700eb124df693a13ad10b42879f715514b129c45bb5f41624f12db2","sha256:31866e4753949b5809d005f2d3e68a3d327de0c0a169d10308fc68bf517e2e84"],"state_sha256":"b49e0bda631a3b1e8f195636d1f39ac046eb42c171b58b7f79464f659fb0bd50"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QigXfNc8n7jUlrjUBdx/RnWc9JVpOJc6ctVo5q8iSfCyxMgTZWwZaTy9ohlLQvFq5iIyUXMXw2J69unD10XrDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:27:29.143948Z","bundle_sha256":"0cfc1aaa2fc694426eb093240cec5fda53498201653e62334ab6a27ac0d89412"}}