{"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/whitening-and-coloring-batch-transform-for","title":"Whitening and Coloring batch transform for GANs","arxiv_id":"1806.00420","date":"2018-06-01","proceeding":"ICLR 2019 5","authors":["Aliaksandr Siarohin","Enver Sangineto","Nicu Sebe"],"abstract":"Batch Normalization (BN) is a common technique used to speed-up and stabilize\ntraining. On the other hand, the learnable parameters of BN are commonly used\nin conditional Generative Adversarial Networks (cGANs) for representing\nclass-specific information using conditional Batch Normalization (cBN). In this\npaper we propose to generalize both BN and cBN using a Whitening and Coloring\nbased batch normalization. We show that our conditional Coloring can represent\ncategorical conditioning information which largely helps the cGAN qualitative\nresults. Moreover, we show that full-feature whitening is important in a\ngeneral GAN scenario in which the training process is known to be highly\nunstable. We test our approach on different datasets and using different GAN\nnetworks and training protocols, showing a consistent improvement in all the\ntested frameworks. Our CIFAR-10 conditioned results are higher than all\nprevious works on this dataset.","url_abs":"http://arxiv.org/abs/1806.00420v2","url_pdf":"http://arxiv.org/pdf/1806.00420v2.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":"whitening-and-coloring-batch-transform-for","repo_url":"https://github.com/AliaksandrSiarohin/wc-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"conditional-batch-normalization","method_name":"Conditional Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.00420","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}