{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CHBRDA4NAHJBRYNE6ZDWKYKA4P","short_pith_number":"pith:CHBRDA4N","canonical_record":{"source":{"id":"1807.00516","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T08:14:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4d1a7e97864b1d63372c6e787e3b13d8aa100c60f658148df8e738c834284f03","abstract_canon_sha256":"6b2c211c67c13d5f8a60dc6a963c39a00893121ef10772cfcc9dda85d5097488"},"schema_version":"1.0"},"canonical_sha256":"11c311838d01d218e1a4f647656140e3de8bf803a27d6cfda2c14a7a62808297","source":{"kind":"arxiv","id":"1807.00516","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00516","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00516v1","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00516","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"pith_short_12","alias_value":"CHBRDA4NAHJB","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CHBRDA4NAHJBRYNE","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CHBRDA4N","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CHBRDA4NAHJBRYNE6ZDWKYKA4P","target":"record","payload":{"canonical_record":{"source":{"id":"1807.00516","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T08:14:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4d1a7e97864b1d63372c6e787e3b13d8aa100c60f658148df8e738c834284f03","abstract_canon_sha256":"6b2c211c67c13d5f8a60dc6a963c39a00893121ef10772cfcc9dda85d5097488"},"schema_version":"1.0"},"canonical_sha256":"11c311838d01d218e1a4f647656140e3de8bf803a27d6cfda2c14a7a62808297","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:52.337149Z","signature_b64":"RgDGu55p5MNuOvnG1hmHxTGr8FeFJmEK/joqdp6zT4j+LGiGdzZkkZha16e/CkIzOIRolp8n2JC1nnKUQSabAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11c311838d01d218e1a4f647656140e3de8bf803a27d6cfda2c14a7a62808297","last_reissued_at":"2026-05-18T00:11:52.336512Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:52.336512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.00516","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":"2026-05-18T00:11:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2jy+ZWUtWqaCaAUCJ5WJcf93SKMP43bwdJ/WMmxqbXQmBjCR/NlCjONYowsERpMAyPCIbaXaw+WquOpCfOhOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T15:43:08.712402Z"},"content_sha256":"cc7c79579a1a45eaf811c87759d2afeb9e94e7cb3eb094a65f2124b611bdb238","schema_version":"1.0","event_id":"sha256:cc7c79579a1a45eaf811c87759d2afeb9e94e7cb3eb094a65f2124b611bdb238"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CHBRDA4NAHJBRYNE6ZDWKYKA4P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Balanced Distribution Adaptation for Transfer Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jindong Wang, Shuji Hao, Wenjie Feng, Yiqiang Chen, Zhiqi Shen","submitted_at":"2018-07-02T08:14:04Z","abstract_excerpt":"Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00516","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":""},"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":"2026-05-18T00:11:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1T6H7m0zEYJpBTXqfexpdpTMG7qZRLfMs1k815lmwiYVw2tZapum7FzsECHR0swwM2Geva79EULqJGA/NgEoCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T15:43:08.713120Z"},"content_sha256":"f813a456a470d83e565a7baf483337ba684d223a553cfc3809bbd51a676facd3","schema_version":"1.0","event_id":"sha256:f813a456a470d83e565a7baf483337ba684d223a553cfc3809bbd51a676facd3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/bundle.json","state_url":"https://pith.science/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/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":"2026-06-06T15:43:08Z","links":{"resolver":"https://pith.science/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P","bundle":"https://pith.science/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/bundle.json","state":"https://pith.science/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CHBRDA4NAHJBRYNE6ZDWKYKA4P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CHBRDA4NAHJBRYNE6ZDWKYKA4P","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":"6b2c211c67c13d5f8a60dc6a963c39a00893121ef10772cfcc9dda85d5097488","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T08:14:04Z","title_canon_sha256":"4d1a7e97864b1d63372c6e787e3b13d8aa100c60f658148df8e738c834284f03"},"schema_version":"1.0","source":{"id":"1807.00516","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00516","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00516v1","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00516","created_at":"2026-05-18T00:11:52Z"},{"alias_kind":"pith_short_12","alias_value":"CHBRDA4NAHJB","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"CHBRDA4NAHJBRYNE","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"CHBRDA4N","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:f813a456a470d83e565a7baf483337ba684d223a553cfc3809bbd51a676facd3","target":"graph","created_at":"2026-05-18T00:11:52Z","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 has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite com","authors_text":"Jindong Wang, Shuji Hao, Wenjie Feng, Yiqiang Chen, Zhiqi Shen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T08:14:04Z","title":"Balanced Distribution Adaptation for Transfer Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00516","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:cc7c79579a1a45eaf811c87759d2afeb9e94e7cb3eb094a65f2124b611bdb238","target":"record","created_at":"2026-05-18T00:11:52Z","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":"6b2c211c67c13d5f8a60dc6a963c39a00893121ef10772cfcc9dda85d5097488","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-02T08:14:04Z","title_canon_sha256":"4d1a7e97864b1d63372c6e787e3b13d8aa100c60f658148df8e738c834284f03"},"schema_version":"1.0","source":{"id":"1807.00516","kind":"arxiv","version":1}},"canonical_sha256":"11c311838d01d218e1a4f647656140e3de8bf803a27d6cfda2c14a7a62808297","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"11c311838d01d218e1a4f647656140e3de8bf803a27d6cfda2c14a7a62808297","first_computed_at":"2026-05-18T00:11:52.336512Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:52.336512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RgDGu55p5MNuOvnG1hmHxTGr8FeFJmEK/joqdp6zT4j+LGiGdzZkkZha16e/CkIzOIRolp8n2JC1nnKUQSabAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:52.337149Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.00516","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc7c79579a1a45eaf811c87759d2afeb9e94e7cb3eb094a65f2124b611bdb238","sha256:f813a456a470d83e565a7baf483337ba684d223a553cfc3809bbd51a676facd3"],"state_sha256":"8546375cfd7b3b006df71bb3c10aad2b0d198ee233ac046da8ecbd067be69dfe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s3wGEprLzWD/JuSlH8AGhoRWKEYC8IDjc71bA1k46Zv0d78u0nrHdbDfUjJvxJyWVRLvW1K4rAmS9iXF65ZGAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T15:43:08.716811Z","bundle_sha256":"e312460a4744f72e46e1c30d288ea894ed47c7444aa0c3be1fcd4c889fddba80"}}