{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4YFQRBXJBOCZ6SHDJZYLDVYTTC","short_pith_number":"pith:4YFQRBXJ","canonical_record":{"source":{"id":"2411.16567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T16:51:11Z","cross_cats_sorted":[],"title_canon_sha256":"ee40e0c9f45a4a69849b8fb8f74af9ebda59beeae866e22eaba20c58c59d6535","abstract_canon_sha256":"7f6f9bb5cd3a0dee31a908e1c51716222cfdc9ec6c7dc3b84bb61488e1f3950a"},"schema_version":"1.0"},"canonical_sha256":"e60b0886e90b859f48e34e70b1d71398b3939a342a9af3cf26db37c70ec0036d","source":{"kind":"arxiv","id":"2411.16567","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16567","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16567v1","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16567","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"4YFQRBXJBOCZ","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_16","alias_value":"4YFQRBXJBOCZ6SHD","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_8","alias_value":"4YFQRBXJ","created_at":"2026-07-05T09:40:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4YFQRBXJBOCZ6SHDJZYLDVYTTC","target":"record","payload":{"canonical_record":{"source":{"id":"2411.16567","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T16:51:11Z","cross_cats_sorted":[],"title_canon_sha256":"ee40e0c9f45a4a69849b8fb8f74af9ebda59beeae866e22eaba20c58c59d6535","abstract_canon_sha256":"7f6f9bb5cd3a0dee31a908e1c51716222cfdc9ec6c7dc3b84bb61488e1f3950a"},"schema_version":"1.0"},"canonical_sha256":"e60b0886e90b859f48e34e70b1d71398b3939a342a9af3cf26db37c70ec0036d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:11.144558Z","signature_b64":"aqgU3vlFoWSRB4SOoe5lQKtHk/Eqw64zwAS6zbms5S7D7NJ5h0tamgUUURVq3BlCs4lE+vHWVmcN3OA3ZcTfCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e60b0886e90b859f48e34e70b1d71398b3939a342a9af3cf26db37c70ec0036d","last_reissued_at":"2026-07-05T09:40:11.144016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:11.144016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.16567","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":"2026-07-05T09:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LPwznSzhZkvB9ZPcE4Snu4FZ5iAoW1wb1sB7FmKzeB/ivfLTlSHYbRk0FpnFXScTWqD5rETlaZTtyZXHB1WuAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T05:24:57.294436Z"},"content_sha256":"a0d299fd717d77069f9c862020f6838003140ef0b7b64e65328ea1571bc7b875","schema_version":"1.0","event_id":"sha256:a0d299fd717d77069f9c862020f6838003140ef0b7b64e65328ea1571bc7b875"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4YFQRBXJBOCZ6SHDJZYLDVYTTC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aoran Shen, Jiacheng Hu, Junliang Du, Shiru Wang, Yingbin Liang, Yinqiu Feng","submitted_at":"2024-11-25T16:51:11Z","abstract_excerpt":"This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16567","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/2411.16567/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":"2026-07-05T09:40:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4dFdSG3sMzSCNEdFX7iezXNs2tmJqbzTyTqE4X0Mj2T/KxGlXL4Qn9TyN7KRR2abNtdPX+WESyxnfxm8OHM1Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T05:24:57.294959Z"},"content_sha256":"bc735a7aff95d986f82f7a8e76e1d4dc8854df0a4475a648c7ac62cf87ab9629","schema_version":"1.0","event_id":"sha256:bc735a7aff95d986f82f7a8e76e1d4dc8854df0a4475a648c7ac62cf87ab9629"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/bundle.json","state_url":"https://pith.science/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/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":"2026-08-12T05:24:57Z","links":{"resolver":"https://pith.science/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC","bundle":"https://pith.science/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/bundle.json","state":"https://pith.science/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4YFQRBXJBOCZ6SHDJZYLDVYTTC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4YFQRBXJBOCZ6SHDJZYLDVYTTC","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":"7f6f9bb5cd3a0dee31a908e1c51716222cfdc9ec6c7dc3b84bb61488e1f3950a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T16:51:11Z","title_canon_sha256":"ee40e0c9f45a4a69849b8fb8f74af9ebda59beeae866e22eaba20c58c59d6535"},"schema_version":"1.0","source":{"id":"2411.16567","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.16567","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"arxiv_version","alias_value":"2411.16567v1","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16567","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_12","alias_value":"4YFQRBXJBOCZ","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_16","alias_value":"4YFQRBXJBOCZ6SHD","created_at":"2026-07-05T09:40:11Z"},{"alias_kind":"pith_short_8","alias_value":"4YFQRBXJ","created_at":"2026-07-05T09:40:11Z"}],"graph_snapshots":[{"event_id":"sha256:bc735a7aff95d986f82f7a8e76e1d4dc8854df0a4475a648c7ac62cf87ab9629","target":"graph","created_at":"2026-07-05T09:40:11Z","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/2411.16567/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, th","authors_text":"Aoran Shen, Jiacheng Hu, Junliang Du, Shiru Wang, Yingbin Liang, Yinqiu Feng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T16:51:11Z","title":"Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16567","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:a0d299fd717d77069f9c862020f6838003140ef0b7b64e65328ea1571bc7b875","target":"record","created_at":"2026-07-05T09:40:11Z","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":"7f6f9bb5cd3a0dee31a908e1c51716222cfdc9ec6c7dc3b84bb61488e1f3950a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T16:51:11Z","title_canon_sha256":"ee40e0c9f45a4a69849b8fb8f74af9ebda59beeae866e22eaba20c58c59d6535"},"schema_version":"1.0","source":{"id":"2411.16567","kind":"arxiv","version":1}},"canonical_sha256":"e60b0886e90b859f48e34e70b1d71398b3939a342a9af3cf26db37c70ec0036d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e60b0886e90b859f48e34e70b1d71398b3939a342a9af3cf26db37c70ec0036d","first_computed_at":"2026-07-05T09:40:11.144016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:40:11.144016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aqgU3vlFoWSRB4SOoe5lQKtHk/Eqw64zwAS6zbms5S7D7NJ5h0tamgUUURVq3BlCs4lE+vHWVmcN3OA3ZcTfCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:40:11.144558Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.16567","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0d299fd717d77069f9c862020f6838003140ef0b7b64e65328ea1571bc7b875","sha256:bc735a7aff95d986f82f7a8e76e1d4dc8854df0a4475a648c7ac62cf87ab9629"],"state_sha256":"9b398e74f94d520c18b8c508f0b1fa608ec6795e1a8963963290d831bcb6881a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bWy5POAv3wo3u6USR4jwfkH2Coaw4BaQaI6RKzHi6gVugNtIBVdgBjmBnZ63CfaEyLk2gDBoPs0PSqgARCxeDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T05:24:57.299176Z","bundle_sha256":"0546a5fc47db17ddb35265e07f54242f33b88166de1e84bfdcee898d940906c1"}}