Probabilistic generative models can be used for compression, denoising, inpainting, texture
synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given …

Generative models, in particular generative adversarial networks (GANs), have gained
significant attention in recent years. A number of GAN variants have been proposed and …

Generative adversarial networks (GAN) are a powerful subclass of generative models.
Despite a very rich research activity leading to numerous interesting GAN algorithms, it is …

Highlights•Evolution: does size matter?•Dynamical factors shaping the layout of visual
response properties.•The role of patterned visual input in development and the issue of …

To overcome the difficulties of sub-band coefficients selection in multiscale transform
domain-based image fusion and solve the problem of block effects suffered by spatial …

Neural codes are believed to have adapted to the statistical properties of the natural
environment. However, the principles that govern the organization of ensemble activity in the …

To isolate the neural mechanisms associated with recognizing objects from those
processing basic visual properties, control stimuli are required that contain the same …

By providing substantial amounts of data and standardized evaluation protocols, datasets in
computer vision have helped fuel advances across all areas of visual recognition. But even …

Abstract Generative Adversarial Network (GAN) has become an active research field due to
its capability to generate quality simulation data. However, two consistent distributions …

The perceptual quality of digital images is often deteriorated during storage, compression,
and transmission. The most reliable way of assessing image quality is to ask people to …