Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟬𝟰 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗿𝘁𝗚𝗔𝗡:Artwork Synthesis with Conditional Categorical GANs by Shinshu University, Nagano, Japan and University of Malaya, Malaysia Follow me for a similar post:  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in #ICIP2017 with over 79 citations. 🔸 It outperforms DCGAN, GAE/VAE, etc. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/edgwYkA Code : https://lnkd.in/e9kjT93 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ArtGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces.  🔸 The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. 🔸 With the feedback from the label information, the generator is able to learn faster and achieve better-generated image quality. Empirically, we show that the proposed ArtGAN is capable to create realistic artwork, as well as generate compelling real-world images that globally look natural with clear shape on CIFAR-10. #computervision #artificialintelligence #deeplearning