Open AccessPosted Content
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium
Martin Heusel,Hubert Ramsauer,Thomas Unterthiner,Bernhard Nessler,Günter Klambauer,Sepp Hochreiter +5 more
TL;DR: In this article, a two time-scale update rule (TTUR) was proposed for training GANs with stochastic gradient descent on arbitrary GAN loss functions, which has an individual learning rate for both the discriminator and the generator.
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Abstract: Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.
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Citations
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
Chaoning Zhang,Chenshuang Zhang,Sheng Zheng,Yu Qiao,Chenghao Li,Mengchun Zhang,Sumit Kumar Dam,Chu Myaet Thwal,Ye Lin Tun,Le Luang Huy,Donguk kim,Sung-Ho Bae,Lik-Hang Lee,Yang Yang,Heng Tao Shen,In So Kweon,Choong Seon Hong +16 more
TL;DR: In this article , a comprehensive review of existing generative AI tasks is provided, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future.
Better Diffusion Models Further Improve Adversarial Training
TL;DR: DM-Improves-AT as mentioned in this paper improves adversarial training by employing the most recent diffusion model which has higher efficiency and image quality (lower FID score) compared with DDPM.
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•Posted Content
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
TL;DR: A new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration, which makes full use of the semantic and pixel space information from different scales of input pairs and pretrain a face parsing network which can generate decent parsing maps from real-world LQ face images.
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Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions
TL;DR: This paper highlights key trends, challenges, and opportunities underpinning the Computer-Aided Molecular Design problems and surveys the current state-of-the-art applications of deep learning to molecular design as a fertile approach towards overcoming computational limitations and navigating uncharted territories of the chemical space.
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DiffIR: Efficient Diffusion Model for Image Restoration
Bin Xia,Yulun Zhang,Shi-Shen Wang,Yitong Wang,Xifeng Wu,Yapeng Tian,Wenming Yang,Luc Van Gool +7 more
TL;DR: Li et al. as discussed by the authors proposed an efficient diffusion model for IR, which consists of a compact IR prior extraction network (CPEN), dynamic IR transformer (DIRformer), and denoising network.
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