Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our
pSp framework is based on a novel encoder network that directly generates a series of style …

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture
for image generation, using models pretrained on several different datasets. We first show …

A rich set of interpretable dimensions has been shown to emerge in the latent space of the
Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify …

This paper describes a simple technique to analyze Generative Adversarial Networks
(GANs) and create interpretable controls for image synthesis, such as change of viewpoint …

In this work, we propose TediGAN, a novel framework for multi-modal image generation and
manipulation with textual descriptions. The proposed method consists of three components …

Recent work has shown that a variety of semantics emerge in the latent space of Generative
Adversarial Networks (GANs) when being trained to synthesize images. However, it is …

Although generative adversarial networks (GANs) have made significant progress in face
synthesis, there lacks enough understanding of what GANs have learned in the latent …

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity
image synthesis, there lacks enough understanding of how GANs are able to map a latent …