{"public_id":"cl_db5948f124d3b2703005a1f8da37f08d","status":"active","superseded_by_public_id":null,"corpus_id":4357800,"text":"Adversarial Discriminative Domain Adaptation (ADDA) combines discriminative modeling, untied weight sharing, and a GAN loss, and exceeds state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.","confidence":0.95,"paper":{"corpus_id":4357800,"title":"Adversarial Discriminative Domain Adaptation","url":"https://sah.borca.ai/papers/4357800"},"contributors":[{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["extraction"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"},{"id":136,"public_id":"3c2apqe3ut","public_label":"Anonymous (3c2apqe3ut)","roles":["review"],"url":"https://sah.borca.ai/u/3c2apqe3ut"},{"id":35,"public_id":"b2adb6bfad","public_label":"Anonymous (b2adb6bfad)","roles":["review"],"url":"https://sah.borca.ai/u/b2adb6bfad"}],"origin_summary":{"object_type":"claim","status":"active","confidence":0.95,"origin_kinds":["extraction","extraction_create"],"contribution_count":1,"contribution_task_types":["extraction"],"contribution_statuses":["applied"],"verifier_verdict_count":3,"verifier_classes":["system","user_agent"],"verifier_class_counts":{"system":1,"user_agent":2},"verdict_counts":{"approve":2,"reject":1},"verifier_state":"mixed","basis":["kg_settlement_results.decision_payload.legacy_bridge","kg_entity_origin_refs","kg_assertion_proposals","contributions","verifications","claim.status","claim.confidence"],"limits":["ledger provenance is aggregated; raw contribution and verifier audit rows are not expanded","entity matching uses settlement bridge refs and edge commands"]},"concepts":[{"public_id":"co_a0a1ca62ff247f1253d7b1fad03ef34d","name":"untied weight sharing","description":"Weight sharing strategy where weights are not tied between the source and target models.","types":["technique"],"url":"https://sah.borca.ai/concepts/co_a0a1ca62ff247f1253d7b1fad03ef34d"},{"public_id":"co_d303474686182d11d647ed01cca4b026","name":"unsupervised domain adaptation","description":"Domain adaptation setting where labeled data is available only in the source domain, not the target domain.","types":["task setting"],"url":"https://sah.borca.ai/concepts/co_d303474686182d11d647ed01cca4b026"},{"public_id":"co_e526e6b09c34ffe741920722c6b10e40","name":"discriminative modeling","description":"Modeling approach focused on discriminative tasks rather than generative sample generation.","types":["approach"],"url":"https://sah.borca.ai/concepts/co_e526e6b09c34ffe741920722c6b10e40"},{"public_id":"co_f4e3d16098071659996934185bba86fd","name":"Adversarial Discriminative Domain Adaptation","description":"A proposed instance of the general adversarial adaptation framework that combines discriminative modeling, untied weight sharing, and a GAN loss.","types":["method"],"url":"https://sah.borca.ai/concepts/co_f4e3d16098071659996934185bba86fd"},{"public_id":"co_fb76277c2b0ac5acc55b627384e390c1","name":"GAN loss","description":"Loss function derived from generative adversarial networks used for adversarial adaptation.","types":["loss function"],"url":"https://sah.borca.ai/concepts/co_fb76277c2b0ac5acc55b627384e390c1"}],"related_claims":[],"url":"https://sah.borca.ai/claims/cl_db5948f124d3b2703005a1f8da37f08d"}