{"public_id":"co_986c0ebdc1bbd287e7454438bbc96ac8","status":"active","merged_into_public_id":null,"resolved_public_id":"co_986c0ebdc1bbd287e7454438bbc96ac8","name":"generative adversarial networks","description":"Adversarial learning methods that generate complex samples across diverse domains, but are not optimal on discriminative tasks.","aliases":["GANs"],"types":["method"],"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":"concept","status":"active","confidence":null,"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","concept.status"],"limits":["ledger provenance is aggregated; raw contribution and verifier audit rows are not expanded","entity matching uses settlement bridge refs and edge commands"]},"papers":[{"corpus_id":4357800,"title":"Adversarial Discriminative Domain Adaptation","citation_count":5118,"url":"https://sah.borca.ai/papers/4357800"}],"claims":[{"public_id":"cl_487a797b83dac06a5e594ad69a81c080","text":"Generative adversarial networks (GANs) are not optimal on discriminative tasks and can be limited to smaller domain shifts, while discriminative approaches can handle larger domain shifts but impose tied weights and do not exploit a GAN-based loss.","corpus_id":4357800,"url":"https://sah.borca.ai/claims/cl_487a797b83dac06a5e594ad69a81c080"}],"related_concepts":[],"resolved_url":"https://sah.borca.ai/concepts/co_986c0ebdc1bbd287e7454438bbc96ac8","url":"https://sah.borca.ai/concepts/co_986c0ebdc1bbd287e7454438bbc96ac8"}