{"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/adversarial-feature-matching-for-text","title":"Adversarial Feature Matching for Text Generation","arxiv_id":"1706.03850","date":"2017-06-12","proceeding":"ICML 2017 8","authors":["Yizhe Zhang","Zhe Gan","Kai Fan","Zhi Chen","Ricardo Henao","Dinghan Shen","Lawrence Carin"],"abstract":"The Generative Adversarial Network (GAN) has achieved great success in\ngenerating realistic (real-valued) synthetic data. However, convergence issues\nand difficulties dealing with discrete data hinder the applicability of GAN to\ntext. We propose a framework for generating realistic text via adversarial\ntraining. We employ a long short-term memory network as generator, and a\nconvolutional network as discriminator. Instead of using the standard objective\nof GAN, we propose matching the high-dimensional latent feature distributions\nof real and synthetic sentences, via a kernelized discrepancy metric. This\neases adversarial training by alleviating the mode-collapsing problem. Our\nexperiments show superior performance in quantitative evaluation, and\ndemonstrate that our model can generate realistic-looking sentences.","url_abs":"http://arxiv.org/abs/1706.03850v3","url_pdf":"http://arxiv.org/pdf/1706.03850v3.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":"adversarial-feature-matching-for-text","repo_url":"https://github.com/dreasysnail/textGAN_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.03850","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}