{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:V6RGTM6ZILFN27PPPXTE4XQBLU","short_pith_number":"pith:V6RGTM6Z","canonical_record":{"source":{"id":"2109.13016","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-27T12:52:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"78977e72e7e237a79ef77560444b1161e262d5d84a80ab0571089f063b7ef903","abstract_canon_sha256":"6ac968c8410fd659a793674957a0748b22f60396184aa27d9ab70b0758994991"},"schema_version":"1.0"},"canonical_sha256":"afa269b3d942cadd7def7de64e5e015d2641c24a61465a85e7c750ef35ceab7f","source":{"kind":"arxiv","id":"2109.13016","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13016","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13016v2","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13016","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_12","alias_value":"V6RGTM6ZILFN","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_16","alias_value":"V6RGTM6ZILFN27PP","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_8","alias_value":"V6RGTM6Z","created_at":"2026-07-05T05:08:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:V6RGTM6ZILFN27PPPXTE4XQBLU","target":"record","payload":{"canonical_record":{"source":{"id":"2109.13016","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-27T12:52:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"78977e72e7e237a79ef77560444b1161e262d5d84a80ab0571089f063b7ef903","abstract_canon_sha256":"6ac968c8410fd659a793674957a0748b22f60396184aa27d9ab70b0758994991"},"schema_version":"1.0"},"canonical_sha256":"afa269b3d942cadd7def7de64e5e015d2641c24a61465a85e7c750ef35ceab7f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:08:14.288818Z","signature_b64":"xZs/ubxjWuprEU7BOQYJYETgmCnFCVHSkiORwwC9EiIvP+x/32xxFq0nrKKNoGodlTd3tJfxbv7chVOM5wpQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa269b3d942cadd7def7de64e5e015d2641c24a61465a85e7c750ef35ceab7f","last_reissued_at":"2026-07-05T05:08:14.288327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:08:14.288327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.13016","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":"2026-07-05T05:08:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fD9XpOl97CpAInDDfE1CoAzFIJUIqRnI5Clj8cSHjn1gdk+R4U/c2/+pSPfuenvOECPqfKhMYfASueYZ5GVJBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:35:39.486125Z"},"content_sha256":"79b05f3ea5a2198409532d2b8a7d66b2b3043e10dda86c66bb3a608f2917c60a","schema_version":"1.0","event_id":"sha256:79b05f3ea5a2198409532d2b8a7d66b2b3043e10dda86c66bb3a608f2917c60a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:V6RGTM6ZILFN27PPPXTE4XQBLU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Adversarial Discriminative Domain Adaptation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Anh Nguyen, Bac Le, Nghia Le, Thai-Vu Nguyen","submitted_at":"2021-09-27T12:52:50Z","abstract_excerpt":"Domain adaptation is a potential method to train a powerful deep neural network, which can handle the absence of labeled data. More precisely, domain adaptation solving the limitation called dataset bias or domain shift when the training dataset and testing dataset are extremely different. Adversarial adaptation method becoming popular among other domain adaptation methods. Relies on the idea of GAN, adversarial domain adaptation tries to minimize the distribution between training and testing datasets base on the adversarial object. However, some conventional adversarial domain adaptation meth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.13016","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/2109.13016/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:08:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PWDZh+jAI56UlJuPlxFY5haWgR8apE7NoKE4XtXhc25jmxAREeDRZvfINMtfgJTokeTI4F8VJddKazjBFoEwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:35:39.486655Z"},"content_sha256":"79a05d1d29cdc2270ea66d25b9dfc71cd18d175f93f22ec5f56d9836cf9dfd91","schema_version":"1.0","event_id":"sha256:79a05d1d29cdc2270ea66d25b9dfc71cd18d175f93f22ec5f56d9836cf9dfd91"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/bundle.json","state_url":"https://pith.science/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/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":"2026-08-23T22:35:39Z","links":{"resolver":"https://pith.science/pith/V6RGTM6ZILFN27PPPXTE4XQBLU","bundle":"https://pith.science/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/bundle.json","state":"https://pith.science/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V6RGTM6ZILFN27PPPXTE4XQBLU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:V6RGTM6ZILFN27PPPXTE4XQBLU","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":"6ac968c8410fd659a793674957a0748b22f60396184aa27d9ab70b0758994991","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-27T12:52:50Z","title_canon_sha256":"78977e72e7e237a79ef77560444b1161e262d5d84a80ab0571089f063b7ef903"},"schema_version":"1.0","source":{"id":"2109.13016","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13016","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13016v2","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13016","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_12","alias_value":"V6RGTM6ZILFN","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_16","alias_value":"V6RGTM6ZILFN27PP","created_at":"2026-07-05T05:08:14Z"},{"alias_kind":"pith_short_8","alias_value":"V6RGTM6Z","created_at":"2026-07-05T05:08:14Z"}],"graph_snapshots":[{"event_id":"sha256:79a05d1d29cdc2270ea66d25b9dfc71cd18d175f93f22ec5f56d9836cf9dfd91","target":"graph","created_at":"2026-07-05T05:08:14Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.13016/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Domain adaptation is a potential method to train a powerful deep neural network, which can handle the absence of labeled data. More precisely, domain adaptation solving the limitation called dataset bias or domain shift when the training dataset and testing dataset are extremely different. Adversarial adaptation method becoming popular among other domain adaptation methods. Relies on the idea of GAN, adversarial domain adaptation tries to minimize the distribution between training and testing datasets base on the adversarial object. However, some conventional adversarial domain adaptation meth","authors_text":"Anh Nguyen, Bac Le, Nghia Le, Thai-Vu Nguyen","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-27T12:52:50Z","title":"Semi-Supervised Adversarial Discriminative Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.13016","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":"sha256:79b05f3ea5a2198409532d2b8a7d66b2b3043e10dda86c66bb3a608f2917c60a","target":"record","created_at":"2026-07-05T05:08:14Z","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":"6ac968c8410fd659a793674957a0748b22f60396184aa27d9ab70b0758994991","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-27T12:52:50Z","title_canon_sha256":"78977e72e7e237a79ef77560444b1161e262d5d84a80ab0571089f063b7ef903"},"schema_version":"1.0","source":{"id":"2109.13016","kind":"arxiv","version":2}},"canonical_sha256":"afa269b3d942cadd7def7de64e5e015d2641c24a61465a85e7c750ef35ceab7f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afa269b3d942cadd7def7de64e5e015d2641c24a61465a85e7c750ef35ceab7f","first_computed_at":"2026-07-05T05:08:14.288327Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:08:14.288327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xZs/ubxjWuprEU7BOQYJYETgmCnFCVHSkiORwwC9EiIvP+x/32xxFq0nrKKNoGodlTd3tJfxbv7chVOM5wpQCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:08:14.288818Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.13016","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79b05f3ea5a2198409532d2b8a7d66b2b3043e10dda86c66bb3a608f2917c60a","sha256:79a05d1d29cdc2270ea66d25b9dfc71cd18d175f93f22ec5f56d9836cf9dfd91"],"state_sha256":"18ef32fab7d137e3afffb62ba640443ca58c77950cc04e10d2984b4f5c56622f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l0WATluh0ONe5KzEUrBfRcqJXQynt733Ava1NtVXArk0SnXA0ByUemWEvE3RPz4qdIytvF7zg3M44MyWtDk/Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T22:35:39.490405Z","bundle_sha256":"992701ab013dbb68d2a4f2e375f7d3f5c18366c8f93d3355cd2b77c58e212bb8"}}