{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5H3WXI72HV7TTMOUKMCQNEPYQB","short_pith_number":"pith:5H3WXI72","canonical_record":{"source":{"id":"1809.02176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T20:54:48Z","cross_cats_sorted":[],"title_canon_sha256":"f6b09c76c99109b1619e65adc9fc5f85247a4eb2d29a1fdff4d89880b688577b","abstract_canon_sha256":"5eb02078f6564b3edd419b2b659d2b30c0258c6b4e5f46de9e8438c7a157368f"},"schema_version":"1.0"},"canonical_sha256":"e9f76ba3fa3d7f39b1d453050691f88061e4974fa7a6c7ed5192832fd000c04f","source":{"kind":"arxiv","id":"1809.02176","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02176","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02176v1","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02176","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"pith_short_12","alias_value":"5H3WXI72HV7T","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5H3WXI72HV7TTMOU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5H3WXI72","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5H3WXI72HV7TTMOUKMCQNEPYQB","target":"record","payload":{"canonical_record":{"source":{"id":"1809.02176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T20:54:48Z","cross_cats_sorted":[],"title_canon_sha256":"f6b09c76c99109b1619e65adc9fc5f85247a4eb2d29a1fdff4d89880b688577b","abstract_canon_sha256":"5eb02078f6564b3edd419b2b659d2b30c0258c6b4e5f46de9e8438c7a157368f"},"schema_version":"1.0"},"canonical_sha256":"e9f76ba3fa3d7f39b1d453050691f88061e4974fa7a6c7ed5192832fd000c04f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:17.746288Z","signature_b64":"9B61QGV0arN2IBObWb8ba3V9DUKF6DlGtgNkAtptvzqia6iZtcE40y+l3lvKphv4zRVU8XZSZri8Re6yJZ0xCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9f76ba3fa3d7f39b1d453050691f88061e4974fa7a6c7ed5192832fd000c04f","last_reissued_at":"2026-05-18T00:06:17.745770Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:17.745770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.02176","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":"2026-05-18T00:06:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UacKCTdmywS8P2t0ZQoetJQXMBzUPQ0qChVSRtJ+OEEIPnpKzj4/MFpb+GqQNlSRMuuKqGhpwLEA6IR3KYPYDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:30:11.835812Z"},"content_sha256":"aad5b6403032d26ad0201cee123de831bcac282d0f0424e3331cf81a3e36073a","schema_version":"1.0","event_id":"sha256:aad5b6403032d26ad0201cee123de831bcac282d0f0424e3331cf81a3e36073a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5H3WXI72HV7TTMOUKMCQNEPYQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Adversarial Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianmin Wang, Mingsheng Long, Zhangjie Cao, Zhongyi Pei","submitted_at":"2018-09-04T20:54:48Z","abstract_excerpt":"Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02176","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":"2026-05-18T00:06:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qQ1yCdMlax0/eSR8upqqDiKU307YjR42OBINzEQLzHvg/G/UwCrGoGU6l+zXaIE3DXnwbOqto8B1H0+muYmiCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T22:30:11.836362Z"},"content_sha256":"8216f361d5b66a3c98ef034ec64c7aa8ebbf00cf01002d8b1ef508860d067fdc","schema_version":"1.0","event_id":"sha256:8216f361d5b66a3c98ef034ec64c7aa8ebbf00cf01002d8b1ef508860d067fdc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/bundle.json","state_url":"https://pith.science/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/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":"2026-05-23T22:30:11Z","links":{"resolver":"https://pith.science/pith/5H3WXI72HV7TTMOUKMCQNEPYQB","bundle":"https://pith.science/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/bundle.json","state":"https://pith.science/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5H3WXI72HV7TTMOUKMCQNEPYQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5H3WXI72HV7TTMOUKMCQNEPYQB","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":"5eb02078f6564b3edd419b2b659d2b30c0258c6b4e5f46de9e8438c7a157368f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T20:54:48Z","title_canon_sha256":"f6b09c76c99109b1619e65adc9fc5f85247a4eb2d29a1fdff4d89880b688577b"},"schema_version":"1.0","source":{"id":"1809.02176","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02176","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02176v1","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02176","created_at":"2026-05-18T00:06:17Z"},{"alias_kind":"pith_short_12","alias_value":"5H3WXI72HV7T","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5H3WXI72HV7TTMOU","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5H3WXI72","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:8216f361d5b66a3c98ef034ec64c7aa8ebbf00cf01002d8b1ef508860d067fdc","target":"graph","created_at":"2026-05-18T00:06:17Z","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":"Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on mul","authors_text":"Jianmin Wang, Mingsheng Long, Zhangjie Cao, Zhongyi Pei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T20:54:48Z","title":"Multi-Adversarial Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02176","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:aad5b6403032d26ad0201cee123de831bcac282d0f0424e3331cf81a3e36073a","target":"record","created_at":"2026-05-18T00:06:17Z","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":"5eb02078f6564b3edd419b2b659d2b30c0258c6b4e5f46de9e8438c7a157368f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-04T20:54:48Z","title_canon_sha256":"f6b09c76c99109b1619e65adc9fc5f85247a4eb2d29a1fdff4d89880b688577b"},"schema_version":"1.0","source":{"id":"1809.02176","kind":"arxiv","version":1}},"canonical_sha256":"e9f76ba3fa3d7f39b1d453050691f88061e4974fa7a6c7ed5192832fd000c04f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9f76ba3fa3d7f39b1d453050691f88061e4974fa7a6c7ed5192832fd000c04f","first_computed_at":"2026-05-18T00:06:17.745770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:17.745770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9B61QGV0arN2IBObWb8ba3V9DUKF6DlGtgNkAtptvzqia6iZtcE40y+l3lvKphv4zRVU8XZSZri8Re6yJZ0xCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:17.746288Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.02176","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aad5b6403032d26ad0201cee123de831bcac282d0f0424e3331cf81a3e36073a","sha256:8216f361d5b66a3c98ef034ec64c7aa8ebbf00cf01002d8b1ef508860d067fdc"],"state_sha256":"5e8db85a98a7cbc48bc91ab15549ff70b2bf0f4f915a9ec52a7a32e3c4232b5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ZvKxq9dUg80sI0spLLRkxnrzg5o9hl4k7btXUmH9izNjkQK1FexdxfLVmm7GPmifrxZ3qfrrTzRHA3h1CzZCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T22:30:11.839234Z","bundle_sha256":"5dbc919d8d58f323b52d3067bc0586726e82347edf2b7f1622b96f33168b228b"}}