{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:GHPOZWJNW423VEC33VIVV7BSSK","short_pith_number":"pith:GHPOZWJN","canonical_record":{"source":{"id":"2201.11333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T05:51:36Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"905e701559607db384418594752a8b5612ecc2817bb80cfa065c0084a65c7004","abstract_canon_sha256":"ac65894330a7c9859c7f52cc33187f5f2877e6056929851b13e18af121592728"},"schema_version":"1.0"},"canonical_sha256":"31deecd92db735ba905bdd515afc32929cb0d30c1780a9b8b8711f1a564ddaf9","source":{"kind":"arxiv","id":"2201.11333","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.11333","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2201.11333v1","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11333","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"GHPOZWJNW423","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"GHPOZWJNW423VEC3","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"GHPOZWJN","created_at":"2026-07-05T04:30:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:GHPOZWJNW423VEC33VIVV7BSSK","target":"record","payload":{"canonical_record":{"source":{"id":"2201.11333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T05:51:36Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"905e701559607db384418594752a8b5612ecc2817bb80cfa065c0084a65c7004","abstract_canon_sha256":"ac65894330a7c9859c7f52cc33187f5f2877e6056929851b13e18af121592728"},"schema_version":"1.0"},"canonical_sha256":"31deecd92db735ba905bdd515afc32929cb0d30c1780a9b8b8711f1a564ddaf9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:30:40.426027Z","signature_b64":"JsIxrpHYs5VcfPtVWiK1eHA5aRb3EjS6tiVxOKOzWaUcFzcxHNcO2U1gZPlXt+BfXObSp64q9Nsi2ZFjqwg3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31deecd92db735ba905bdd515afc32929cb0d30c1780a9b8b8711f1a564ddaf9","last_reissued_at":"2026-07-05T04:30:40.425615Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:30:40.425615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.11333","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":"2026-07-05T04:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RbrnxK5cz9B9cdJOXaxWNUGbK8VEKGkTyzTcTi9z4u8DgQv25DuGe58AfB4KWVTuscCwGkEtlNarIAgnLvUEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:09:22.576426Z"},"content_sha256":"e9f488e33394542227e3580f0b0a94246f3b49d975039b39e48bd5e06a2271ff","schema_version":"1.0","event_id":"sha256:e9f488e33394542227e3580f0b0a94246f3b49d975039b39e48bd5e06a2271ff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:GHPOZWJNW423VEC33VIVV7BSSK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Aydogan Ozcan, Luzhe Huang, Tairan Liu, Xilin Yang","submitted_at":"2022-01-27T05:51:36Z","abstract_excerpt":"Deep learning-based methods in computational microscopy have been shown to be powerful but in general face some challenges due to limited generalization to new types of samples and requirements for large and diverse training data. Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets. We pre-trained a convolutional recurrent neural network on a large dataset with diverse types of samples, which serves as the backbone model. By fixing the recurrent blocks and tran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11333","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.11333/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CGDXOFTtZXzaHk/ctqkHMaNtsgPaNIFjZi+9l/02X/zn6HJGV/Qz/odR9wJjuZa55oX8OHTyUw0uDLzIbQVEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:09:22.576795Z"},"content_sha256":"3fed39f378214a95087c6fcbd186c8c520ed487cd87addbfca6a63b8eab9f6e2","schema_version":"1.0","event_id":"sha256:3fed39f378214a95087c6fcbd186c8c520ed487cd87addbfca6a63b8eab9f6e2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GHPOZWJNW423VEC33VIVV7BSSK/bundle.json","state_url":"https://pith.science/pith/GHPOZWJNW423VEC33VIVV7BSSK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GHPOZWJNW423VEC33VIVV7BSSK/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":"2026-07-07T11:09:22Z","links":{"resolver":"https://pith.science/pith/GHPOZWJNW423VEC33VIVV7BSSK","bundle":"https://pith.science/pith/GHPOZWJNW423VEC33VIVV7BSSK/bundle.json","state":"https://pith.science/pith/GHPOZWJNW423VEC33VIVV7BSSK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GHPOZWJNW423VEC33VIVV7BSSK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GHPOZWJNW423VEC33VIVV7BSSK","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":"ac65894330a7c9859c7f52cc33187f5f2877e6056929851b13e18af121592728","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T05:51:36Z","title_canon_sha256":"905e701559607db384418594752a8b5612ecc2817bb80cfa065c0084a65c7004"},"schema_version":"1.0","source":{"id":"2201.11333","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.11333","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"2201.11333v1","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11333","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"GHPOZWJNW423","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_16","alias_value":"GHPOZWJNW423VEC3","created_at":"2026-07-05T04:30:40Z"},{"alias_kind":"pith_short_8","alias_value":"GHPOZWJN","created_at":"2026-07-05T04:30:40Z"}],"graph_snapshots":[{"event_id":"sha256:3fed39f378214a95087c6fcbd186c8c520ed487cd87addbfca6a63b8eab9f6e2","target":"graph","created_at":"2026-07-05T04:30:40Z","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/2201.11333/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning-based methods in computational microscopy have been shown to be powerful but in general face some challenges due to limited generalization to new types of samples and requirements for large and diverse training data. Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets. We pre-trained a convolutional recurrent neural network on a large dataset with diverse types of samples, which serves as the backbone model. By fixing the recurrent blocks and tran","authors_text":"Aydogan Ozcan, Luzhe Huang, Tairan Liu, Xilin Yang","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T05:51:36Z","title":"Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11333","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:e9f488e33394542227e3580f0b0a94246f3b49d975039b39e48bd5e06a2271ff","target":"record","created_at":"2026-07-05T04:30:40Z","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":"ac65894330a7c9859c7f52cc33187f5f2877e6056929851b13e18af121592728","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-01-27T05:51:36Z","title_canon_sha256":"905e701559607db384418594752a8b5612ecc2817bb80cfa065c0084a65c7004"},"schema_version":"1.0","source":{"id":"2201.11333","kind":"arxiv","version":1}},"canonical_sha256":"31deecd92db735ba905bdd515afc32929cb0d30c1780a9b8b8711f1a564ddaf9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31deecd92db735ba905bdd515afc32929cb0d30c1780a9b8b8711f1a564ddaf9","first_computed_at":"2026-07-05T04:30:40.425615Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:30:40.425615Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JsIxrpHYs5VcfPtVWiK1eHA5aRb3EjS6tiVxOKOzWaUcFzcxHNcO2U1gZPlXt+BfXObSp64q9Nsi2ZFjqwg3Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:30:40.426027Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.11333","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e9f488e33394542227e3580f0b0a94246f3b49d975039b39e48bd5e06a2271ff","sha256:3fed39f378214a95087c6fcbd186c8c520ed487cd87addbfca6a63b8eab9f6e2"],"state_sha256":"e1a408b20141b2cfce1694d7c77d438d14861e8dcdd015cbecc6e73931ab8e0f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UBNx03oTuuEq4sXzmTBVX78Hziusdc0Z30ooTgSySHcvMPrw+xBnG4FSS9GL94DjFa1gMov5tHU78mPGgzRJCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:09:22.578643Z","bundle_sha256":"fe4c59319069d4c5eafa855e2aacfe5aa16e9584f9c08bc88ebe5b278b54fd18"}}