Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟮𝟯 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 State-of-the-Art in the Architecture, Methods and Applications of StyleGAN by Tel Aviv University Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published arxiv2022. 🪐 StyleGAN has revolutionized the field of image synthesis, bring- ing with it consistent, high-quality results with exceptional photo- realism across multiple domains. 🪐 More interestingly, through a com- bination of layer-wise style modulations and a novel mapping net- work, StyleGAN is capable of mapping out a smooth, semantic, and highly-disentangled latent space in an entirely unsupervised manner. 🪐 This enables latent-based editing, yielding effects such as photo-realistic and plausible alterations to age, hairstyles, or body poses, and even transformations into celebrities or magical beings. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ➡️ Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. ➡️ This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. ➡️ It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. ➡️ Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. ➡️ Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. ➡️ Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. ➡️ Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. ➡️ We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. ➡️ We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning. #computervision #artificialintelligence #data