{"id":83868,"date":"2018-10-22T17:42:21","date_gmt":"2018-10-23T00:42:21","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2018\/10\/binarygan-a-generative-adversarial-network-with-binary-neurons"},"modified":"2018-10-25T15:22:31","modified_gmt":"2018-10-25T22:22:31","slug":"binarygan-a-generative-adversarial-network-with-binary-neurons","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2018\/10\/binarygan-a-generative-adversarial-network-with-binary-neurons","title":{"rendered":"BinaryGAN: a generative adversarial network with binary neurons"},"content":{"rendered":"

<\/a><\/p>\n

Researchers at the Research Center for IT Innovation of Academia Sinica, in Taiwan, have recently developed a novel generative adversarial network (GAN) that has binary neurons at the output layer of the generator. This model, presented in a paper pre-published on arXiv<\/a>, can directly generate binary-valued predictions at test time.<\/p>\n

So far, GAN approaches have achieved remarkable results in modeling continuous distributions. Nonetheless, applying GANs to discrete data has been somewhat challenging so far, particularly due to difficulties in optimizing the model<\/a> distribution toward the target data distribution in a high-dimensional discrete space.<\/p>\n

Hao-Wen Dong, one of the researchers who carried out the study, told Tech Xplore<\/i>, \u201cI am currently working on music generation in the Music and AI Lab at Academia Sinica. In my opinion, composing can be interpreted as a series of decisions\u2014for instance, regarding the instrumentation, chords and even the exact notes to use. To move toward achieving the grand vision of a solid AI composer, I am particularly interested in whether deep generative models such as GANs are able to make decisions. Therefore, this work examined whether we can train a GAN that uses binary neurons to make binary decisions using backpropagation, the standard training algorithm.\u201d<\/p>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at the Research Center for IT Innovation of Academia Sinica, in Taiwan, have recently developed a novel generative adversarial network (GAN) that has binary neurons at the output layer of the generator. This model, presented in a paper pre-published on arXiv, can directly generate binary-valued predictions at test time. So far, GAN approaches have [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41,42,6,8],"tags":[],"class_list":["post-83868","post","type-post","status-publish","format-standard","hentry","category-information-science","category-media-arts","category-robotics-ai","category-space"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/83868","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=83868"}],"version-history":[{"count":1,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/83868\/revisions"}],"predecessor-version":[{"id":84044,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/83868\/revisions\/84044"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=83868"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=83868"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=83868"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}