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

Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a
zero-shot classifier by discrete prompt design, eg, the confidence score of an image …

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 …

Abstract Generative Adversarial Network (GAN) is a research hotspot in deep generative
models, which has been widely used in the field of medical image fusion. This paper …

Abstract We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm
for offline reinforcement learning (RL) under insufficient data coverage, based on the …

Recently, Vision Transformers (ViTs) have shown competitive performance on image
recognition while requiring less vision-specific inductive biases. In this paper, we investigate …

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a
batch of previously collected data. This problem setting is compelling, because it offers 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 …

We investigate the training and performance of generative adversarial networks using the
Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical …