{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MA2GFQB6FFHKB5IW46NRT72J6G","short_pith_number":"pith:MA2GFQB6","canonical_record":{"source":{"id":"1904.01376","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T12:43:53Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"b9a8b0bca93bccafc23a1bd8d99918230359e6605fe622947af0937344fa0e9c","abstract_canon_sha256":"6b38100227320896656d1003f9e093183ba4337ad7e55af17fff0039c0649624"},"schema_version":"1.0"},"canonical_sha256":"603462c03e294ea0f516e79b19ff49f1819c6d67f7a76258ae3240d89db04719","source":{"kind":"arxiv","id":"1904.01376","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.01376","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"arxiv_version","alias_value":"1904.01376v2","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01376","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"pith_short_12","alias_value":"MA2GFQB6FFHK","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MA2GFQB6FFHKB5IW","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MA2GFQB6","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MA2GFQB6FFHKB5IW46NRT72J6G","target":"record","payload":{"canonical_record":{"source":{"id":"1904.01376","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T12:43:53Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"b9a8b0bca93bccafc23a1bd8d99918230359e6605fe622947af0937344fa0e9c","abstract_canon_sha256":"6b38100227320896656d1003f9e093183ba4337ad7e55af17fff0039c0649624"},"schema_version":"1.0"},"canonical_sha256":"603462c03e294ea0f516e79b19ff49f1819c6d67f7a76258ae3240d89db04719","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:54.712946Z","signature_b64":"quCTqdXmp8+w5MOSmNTW8uwOV/PfniBRqVQJyagvx3EuRFOevpgcBH+BXYtLWCrVqEQKomMPw26GklhW2xfaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"603462c03e294ea0f516e79b19ff49f1819c6d67f7a76258ae3240d89db04719","last_reissued_at":"2026-05-17T23:48:54.712286Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:54.712286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.01376","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RUkBnT8H+IZsyLQ6e65TZu03QSGBnufAjPqEAivq1bphpnEnRo8eqPCdPAQU16kuFmb2Ik2tQMeTF1QdAXV2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T02:09:47.399089Z"},"content_sha256":"fb0ce0f10d3cda70ed00d99ca909bafcc7f2fd3d38ea7c870952686b03c7947b","schema_version":"1.0","event_id":"sha256:fb0ce0f10d3cda70ed00d99ca909bafcc7f2fd3d38ea7c870952686b03c7947b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MA2GFQB6FFHKB5IW46NRT72J6G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Easy Transfer Learning By Exploiting Intra-domain Structures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Han Yu, Jindong Wang, Meiyu Huang, Qiang Yang, Yiqiang Chen","submitted_at":"2019-04-02T12:43:53Z","abstract_excerpt":"Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results. Moreover, cross-validation is not possible for tuning hyperparameters since there are often no labels in the target domain. This would restrict wide applicability of transfer learning especially in computationally-constraint devices such as wearables. In this paper, we propose a practically Easy Transfer Learning (EasyTL) ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01376","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":""},"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":"2026-05-17T23:48:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AMpgpP3WAzh83z9Q5LukybRHpAmxSj/Z9GJIwAGfkKuymFhPI5PwKbBw13DKDNGSSB/0TNmBURFaBmfqeadRCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T02:09:47.399582Z"},"content_sha256":"c863dd30f23236c8fa0e2e86e16e1925c44689ff1256cf6a87cfe0d48b6850f5","schema_version":"1.0","event_id":"sha256:c863dd30f23236c8fa0e2e86e16e1925c44689ff1256cf6a87cfe0d48b6850f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MA2GFQB6FFHKB5IW46NRT72J6G/bundle.json","state_url":"https://pith.science/pith/MA2GFQB6FFHKB5IW46NRT72J6G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MA2GFQB6FFHKB5IW46NRT72J6G/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":"2026-05-31T02:09:47Z","links":{"resolver":"https://pith.science/pith/MA2GFQB6FFHKB5IW46NRT72J6G","bundle":"https://pith.science/pith/MA2GFQB6FFHKB5IW46NRT72J6G/bundle.json","state":"https://pith.science/pith/MA2GFQB6FFHKB5IW46NRT72J6G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MA2GFQB6FFHKB5IW46NRT72J6G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MA2GFQB6FFHKB5IW46NRT72J6G","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":"6b38100227320896656d1003f9e093183ba4337ad7e55af17fff0039c0649624","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T12:43:53Z","title_canon_sha256":"b9a8b0bca93bccafc23a1bd8d99918230359e6605fe622947af0937344fa0e9c"},"schema_version":"1.0","source":{"id":"1904.01376","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.01376","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"arxiv_version","alias_value":"1904.01376v2","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01376","created_at":"2026-05-17T23:48:54Z"},{"alias_kind":"pith_short_12","alias_value":"MA2GFQB6FFHK","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MA2GFQB6FFHKB5IW","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MA2GFQB6","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:c863dd30f23236c8fa0e2e86e16e1925c44689ff1256cf6a87cfe0d48b6850f5","target":"graph","created_at":"2026-05-17T23:48:54Z","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":"Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results. Moreover, cross-validation is not possible for tuning hyperparameters since there are often no labels in the target domain. This would restrict wide applicability of transfer learning especially in computationally-constraint devices such as wearables. In this paper, we propose a practically Easy Transfer Learning (EasyTL) ap","authors_text":"Han Yu, Jindong Wang, Meiyu Huang, Qiang Yang, Yiqiang Chen","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T12:43:53Z","title":"Easy Transfer Learning By Exploiting Intra-domain Structures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01376","kind":"arxiv","version":2},"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:fb0ce0f10d3cda70ed00d99ca909bafcc7f2fd3d38ea7c870952686b03c7947b","target":"record","created_at":"2026-05-17T23:48:54Z","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":"6b38100227320896656d1003f9e093183ba4337ad7e55af17fff0039c0649624","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-02T12:43:53Z","title_canon_sha256":"b9a8b0bca93bccafc23a1bd8d99918230359e6605fe622947af0937344fa0e9c"},"schema_version":"1.0","source":{"id":"1904.01376","kind":"arxiv","version":2}},"canonical_sha256":"603462c03e294ea0f516e79b19ff49f1819c6d67f7a76258ae3240d89db04719","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"603462c03e294ea0f516e79b19ff49f1819c6d67f7a76258ae3240d89db04719","first_computed_at":"2026-05-17T23:48:54.712286Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:54.712286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"quCTqdXmp8+w5MOSmNTW8uwOV/PfniBRqVQJyagvx3EuRFOevpgcBH+BXYtLWCrVqEQKomMPw26GklhW2xfaBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:54.712946Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.01376","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb0ce0f10d3cda70ed00d99ca909bafcc7f2fd3d38ea7c870952686b03c7947b","sha256:c863dd30f23236c8fa0e2e86e16e1925c44689ff1256cf6a87cfe0d48b6850f5"],"state_sha256":"99222d023da34cd771dc750a0be822e6e025bd2c4b9a04782d7f5a1498cc317a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lRwFYMFDqtyT3adBdZfs6fCHX5PC7Y5ldCyOGcrtq8HKIAf5O1sKU5GCMKO+zjYYb+QBk2QxJOvmqZ5IGXCMCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T02:09:47.401823Z","bundle_sha256":"f09ed6674663f71f8b0095ecb6900029f09d2bcc72504465bb6aa8fcafd81c0a"}}