{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/f-gan-training-generative-neural-samplers","title":"f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization","arxiv_id":"1606.00709","date":"2016-06-02","proceeding":"NeurIPS 2016 12","authors":["Sebastian Nowozin","Botond Cseke","Ryota Tomioka"],"abstract":"Generative neural samplers are probabilistic models that implement sampling\nusing feedforward neural networks: they take a random input vector and produce\na sample from a probability distribution defined by the network weights. These\nmodels are expressive and allow efficient computation of samples and\nderivatives, but cannot be used for computing likelihoods or for\nmarginalization. The generative-adversarial training method allows to train\nsuch models through the use of an auxiliary discriminative neural network. We\nshow that the generative-adversarial approach is a special case of an existing\nmore general variational divergence estimation approach. We show that any\nf-divergence can be used for training generative neural samplers. We discuss\nthe benefits of various choices of divergence functions on training complexity\nand the quality of the obtained generative models.","url_abs":"http://arxiv.org/abs/1606.00709v1","url_pdf":"http://arxiv.org/pdf/1606.00709v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"f-gan-training-generative-neural-samplers","repo_url":"https://github.com/AlexVerine/PrecisionRecallGan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"f-gan-training-generative-neural-samplers","repo_url":"https://github.com/mboudiaf/Mutual-Information-Variational-Bounds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.00709","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}