A Systematic Survey of Regularization and Normalization in GANs

Generative Adversarial Networks (GANs) have been widely applied in different scenarios thanks to the development of deep neural networks. The original GAN was proposed based on the non-parametric assumption of the infinite capacity of networks. However, it is still unknown whether GANs can generate realistic samples without any prior information. Due to the overconfident assumption, many issues remain unaddressed in GANs' training, such as non-convergence, mode collapses, gradient vanishing. Regularization and normalization are common methods of introducing prior information to stabilize training and improve discrimination. Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey which primarily focuses on objectives and development of these methods, apart from some in-comprehensive and limited scope studies. In this work, we conduct a comprehensive survey on the regularization and normalization techniques from different perspectives of GANs training. First, we systematically describe different perspectives of GANs training and thus obtain the different objectives of regularization and normalization. Based on these objectives, we propose a new taxonomy. Furthermore, we compare the performance of the mainstream methods on different datasets and investigate the regularization and normalization techniques that have been frequently employed in SOTA GANs. Finally, we highlight potential future directions of research in this domain

RUSH: Robust Contrastive Learning via Randomized Smoothing

Recently, adversarial training has been incorporated in self-supervised contrastive pre-training to augment label efficiency with exciting adversarial robustness. However, the robustness came at a cost of expensive adversarial training. In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing. It boosts both standard accuracy and robust accuracy, and significantly reduces training costs as compared with adversarial training. We use extensive empirical studies to show that the proposed RUSH outperforms robust classifiers from adversarial training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100, and STL-10) under first-order attacks. In particular, under $\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9% standard accuracy. Our work has an improvement of over 15% in robust accuracy and a slight improvement in standard accuracy, compared to the state-of-the-arts.Comment: 12 pages, 2 figure

Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments

Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as translucent object parts, and preserve photorealistic object appearance. To support real-time rendering without compromising rendering quality, our model uses a grid of features and a small MLP decoder that is transpiled into efficient shader code with interactive framerates. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects. Thanks to the use of standard shader code rendering is portable across many existing hardware and software systems