Praveen Krishnan, lead author, and post-doc research at Facebook had the following to say:<\/p>\n
Our disentanglement approach is further trained in a self-supervised manner, allowing the use of real photos for training, without style labels.<\/em><\/p>\n Facebook has no qualms in admitting this project is still in the early stages of research. They express their motivation for publicizing their technology as a means to unite researchers to develop technology to help combat future misuse\u2014such as the generation of deepfake technology.<\/p>\n Facebooks TextStyleBrush is largely billed as another stride in developing technology to better combat the rise of deepfake technologies. Deepfakes, a term used to describe artificially generated text, images, and video purporting false realities, are an emerging technological threat relevant on a global scale. Facebook has described their efforts to combat deepfake technology-focused of visual manipulation of human faces in images previously. In this newest advancement, they describe TextStyleBrush’s application in combatting Deepfakes as such:<\/p>\n If AI researchers and practitioners can get ahead of adversaries in building this technology, we can learn to better detect this new style of deepfakes and build robust systems to combat them.<\/em><\/p>\n Another possible application of TextStyleBrush is the real-time translation (both language and data-driven) of textual information in augmented reality environments. Augmented reality represents a particular challenge to traditional AI models that rely on carefully labeled datasets to be trained.<\/p>\nApplications<\/h2>\n
![\"textbrushstyle](\"https:\/\/www.alpharithms.com\/wp-content\/uploads\/893\/textbrushstyle-facebook.jpg\" "\\"textbrushstyle-facebook\\"")