Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

In standard generative adversarial network (SGAN), the discriminator estimates the
probability that the input data is real. The generator is trained to increase the probability that …

What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we
modify a classical GAN, ie., StyleGANv2, as little as possible. We find that only two …

Abstract Generative Adversarial Networks (GANs) excel at creating realistic images with
complex models for which maximum likelihood is infeasible. However, the convergence of …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …

We introduce a method to generate vectorial representations of visual classification tasks
which can be used to reason about the nature of those tasks and their relations. Given a …

Generative Adversarial Networks (GANs) have received wide attention in the machine
learning field for their potential to learn high-dimensional, complex real data distribution …