We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3
adapts denoising diffusion probabilistic models (Ho et al. 2020),(Sohl-Dickstein et al. 2015) …

This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial
autoencoders, and their variants. We start with explaining adversarial learning and the …

Risks and uncertainties are inevitable in construction projects and can drastically change
the expected outcome, negatively impacting the project's success. However, risk …

For all the ways convolutional neural nets have revolutionized computer vision in recent
years, one important aspect has received surprisingly little attention: the effect of image size …

The vast work in Deep Learning (DL) has led to a leap in image denoising research. Most
DL solutions for this task have chosen to put their efforts on the denoiser's architecture while …

Understanding of GAN training is still very limited. One major challenge is its non-convex-
non-concave min-max objective, which may lead to sub-optimal local minima. In this work …

State-of-the-art face restoration methods employ deep convolutional neural networks
(CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local …

Traditional Chinese landscape painting is prone to low-resolution image issues during the
digital protection process. To reconstruct high-quality images from low-resolution landscape …

Generative adversarial networks (GANs) could be used efficiently for image and video
generation when labeled training data is available in bulk. In general, building a good …

Recently, the task of image generation has attracted much attention. In particular, the recent
empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin …