{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5RCJWY2B3CIIVCPHV7ZFQIIHXF","short_pith_number":"pith:5RCJWY2B","schema_version":"1.0","canonical_sha256":"ec449b6341d8908a89e7aff2582107b954adc722bb09a99d24bc8bd327e516de","source":{"kind":"arxiv","id":"2201.09679","version":2},"attestation_state":"computed","paper":{"title":"A Review of Deep Transfer Learning and Recent Advancements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Hamid R. Arabnia, Khaled Rasheed, Mohammadreza Iman","submitted_at":"2022-01-19T04:19:36Z","abstract_excerpt":"Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs by reusing an obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training cos"},"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":"2201.09679","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-19T04:19:36Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"1717cba2f81008404b794d85a02347ef89a9631d7749cee996f6984234fc1196","abstract_canon_sha256":"7e98fa8cbed3b53891d3ebb4d341e174e8fc5bd5b7e8a1f2b454d19566ef3201"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:38.708593Z","signature_b64":"9oXIXRwtrpDCL8+QbulOC1ctaf/7fbZrSRg79Ft3Y67OMuyWLhOxu2bPV2oC/OM3WQaLx6bGCmDCM1tXzma4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec449b6341d8908a89e7aff2582107b954adc722bb09a99d24bc8bd327e516de","last_reissued_at":"2026-07-05T05:50:38.708164Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:38.708164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Review of Deep Transfer Learning and Recent Advancements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Hamid R. Arabnia, Khaled Rasheed, Mohammadreza Iman","submitted_at":"2022-01-19T04:19:36Z","abstract_excerpt":"Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs by reusing an obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training cos"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.09679","kind":"arxiv","version":2},"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/2201.09679/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":"2201.09679","created_at":"2026-07-05T05:50:38.708221+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.09679v2","created_at":"2026-07-05T05:50:38.708221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.09679","created_at":"2026-07-05T05:50:38.708221+00:00"},{"alias_kind":"pith_short_12","alias_value":"5RCJWY2B3CII","created_at":"2026-07-05T05:50:38.708221+00:00"},{"alias_kind":"pith_short_16","alias_value":"5RCJWY2B3CIIVCPH","created_at":"2026-07-05T05:50:38.708221+00:00"},{"alias_kind":"pith_short_8","alias_value":"5RCJWY2B","created_at":"2026-07-05T05:50:38.708221+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.05730","citing_title":"Effective Knowledge Transfer for Multi-Task Recommendation Models","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF","json":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF.json","graph_json":"https://pith.science/api/pith-number/5RCJWY2B3CIIVCPHV7ZFQIIHXF/graph.json","events_json":"https://pith.science/api/pith-number/5RCJWY2B3CIIVCPHV7ZFQIIHXF/events.json","paper":"https://pith.science/paper/5RCJWY2B"},"agent_actions":{"view_html":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF","download_json":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF.json","view_paper":"https://pith.science/paper/5RCJWY2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.09679&json=true","fetch_graph":"https://pith.science/api/pith-number/5RCJWY2B3CIIVCPHV7ZFQIIHXF/graph.json","fetch_events":"https://pith.science/api/pith-number/5RCJWY2B3CIIVCPHV7ZFQIIHXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF/action/storage_attestation","attest_author":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF/action/author_attestation","sign_citation":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF/action/citation_signature","submit_replication":"https://pith.science/pith/5RCJWY2B3CIIVCPHV7ZFQIIHXF/action/replication_record"}},"created_at":"2026-07-05T05:50:38.708221+00:00","updated_at":"2026-07-05T05:50:38.708221+00:00"}