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

Recent technological innovations along with the vast amount of available data worldwide
have led to the rise of cyberattacks against network systems. Intrusion Detection Systems …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

Transformers have recently gained significant attention in the computer vision community.
However, the lack of scalability of self-attention mechanisms with respect to image size has …

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

Generative modeling and representation learning are two key tasks in computer vision.
However, these models are typically trained independently, which ignores the potential for …

Metrics for evaluating generative models aim to measure the discrepancy between real and
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …

The performance of generative adversarial networks (GANs) heavily deteriorates given a
limited amount of training data. This is mainly because the discriminatorsis memorizing the …

Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …

Fairness in visual recognition is becoming a prominent and critical topic of discussion as
recognition systems are deployed at scale in the real world. Models trained from data in …