{"paper":{"title":"Generative Adversarial Networks as Variational Training of Energy Based Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Rogerio Feris, Shuangfei Zhai, Yu Cheng, Zhongfei Zhang","submitted_at":"2016-11-06T16:04:48Z","abstract_excerpt":"In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density $p(\\mathbf{x})$ is approximated by a variational distribution $q(\\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two step procedure: given $p(\\mathbf{x})$, $q(\\mathbf{x})$ is updated to maximize the lower bound; $p(\\mathbf{x})$ is then updated one step with samples drawn from $q(\\mathbf{x})$ to decrease the lower bound. VGAN is i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01799","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"}