{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SCJZC5IIWO7D2EG4N7AS26AWBT","short_pith_number":"pith:SCJZC5II","schema_version":"1.0","canonical_sha256":"9093917508b3be3d10dc6fc12d78160cfd3db037695facd3b1b53ed94d641027","source":{"kind":"arxiv","id":"2205.07124","version":1},"attestation_state":"computed","paper":{"title":"Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Bharath Kumar Bolla, Sabeesh Ethiraj","submitted_at":"2022-05-14T20:08:19Z","abstract_excerpt":"The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transfer learning to classify the SDSS-IV dataset, we further extended our research in the fine tuning of these architectures to study the effect in the classification scenario. Architectures such as Resnet-50, DenseNet-121 VGG-16, Xception, EfficientNetB2, MobileNetV2 and NasnetMobile have been built using layer wise fine tuning at different levels. Our findings suggest that freezing all laye"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2205.07124","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-14T20:08:19Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0affa1bc8c870cb4f98507665edb28d880f53c900a0fa6d10df3b0c5baeb2227","abstract_canon_sha256":"8a4b982030e5ffe92437b1a59a1b493b386f11b6ddee79992b663f608fd3b98c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:23:12.405146Z","signature_b64":"niVzO62fNBH9y04jw9vuX0e1l9uyIQVXNaeBlbE/9q5kjvd4bx73dIBmOmhxLer6FbPew4myhrnUcwmkwwgvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9093917508b3be3d10dc6fc12d78160cfd3db037695facd3b1b53ed94d641027","last_reissued_at":"2026-07-05T04:23:12.404646Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:23:12.404646Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Bharath Kumar Bolla, Sabeesh Ethiraj","submitted_at":"2022-05-14T20:08:19Z","abstract_excerpt":"The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transfer learning to classify the SDSS-IV dataset, we further extended our research in the fine tuning of these architectures to study the effect in the classification scenario. Architectures such as Resnet-50, DenseNet-121 VGG-16, Xception, EfficientNetB2, MobileNetV2 and NasnetMobile have been built using layer wise fine tuning at different levels. Our findings suggest that freezing all laye"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07124","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/2205.07124/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2205.07124","created_at":"2026-07-05T04:23:12.404704+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.07124v1","created_at":"2026-07-05T04:23:12.404704+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07124","created_at":"2026-07-05T04:23:12.404704+00:00"},{"alias_kind":"pith_short_12","alias_value":"SCJZC5IIWO7D","created_at":"2026-07-05T04:23:12.404704+00:00"},{"alias_kind":"pith_short_16","alias_value":"SCJZC5IIWO7D2EG4","created_at":"2026-07-05T04:23:12.404704+00:00"},{"alias_kind":"pith_short_8","alias_value":"SCJZC5II","created_at":"2026-07-05T04:23:12.404704+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT","json":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT.json","graph_json":"https://pith.science/api/pith-number/SCJZC5IIWO7D2EG4N7AS26AWBT/graph.json","events_json":"https://pith.science/api/pith-number/SCJZC5IIWO7D2EG4N7AS26AWBT/events.json","paper":"https://pith.science/paper/SCJZC5II"},"agent_actions":{"view_html":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT","download_json":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT.json","view_paper":"https://pith.science/paper/SCJZC5II","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.07124&json=true","fetch_graph":"https://pith.science/api/pith-number/SCJZC5IIWO7D2EG4N7AS26AWBT/graph.json","fetch_events":"https://pith.science/api/pith-number/SCJZC5IIWO7D2EG4N7AS26AWBT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT/action/storage_attestation","attest_author":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT/action/author_attestation","sign_citation":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT/action/citation_signature","submit_replication":"https://pith.science/pith/SCJZC5IIWO7D2EG4N7AS26AWBT/action/replication_record"}},"created_at":"2026-07-05T04:23:12.404704+00:00","updated_at":"2026-07-05T04:23:12.404704+00:00"}