We offer a practical unpaired learning based image dehazing network from an unpaired set
of clear and hazy images. This paper provides a new perspective to treat image dehazing as …

Abstract Generative Adversarial Networks (GANs) are one of the most efficient generative
models to generate data. They have made breakthroughs in many computer vision tasks …

Graph convolutional networks (GCNs) are powerful tools for analyzing structured data with
entities based on messages passing between a node and its surrounding nodes; these …

Abstract The Controllable Variational Autoencoder (ControlVAE) combines automatic control
theory with the basic VAE model to manipulate the KL-divergence for overcoming posterior …

Variational Autoencoders have gained considerable attention due to their capacity of
encoding high dimensional data into a lower dimensional latent space. In this context …

SCGAN adds a similarity constraint between generated images and conditions as a
regularization term on generative adversarial networks. Similarity constraint works as a tutor …

Generative adversarial networks (GANs) with clustered latent spaces can perform
conditional generation in a completely unsupervised manner. In the real world, the salient …

SCGAN introduces a similarity constraint between generated images and conditions as a
regularization term on generative adversarial networks. This constraint guides the generator …

Deep learning has been widely used in real-life applications during the last few decades,
such as face recognition, machine translation, object detection and classification …