Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

We identify a trade-off between robustness and accuracy that serves as a guiding principle
in the design of defenses against adversarial examples. Although this problem has been …

Abstract Generative Adversarial Networks (GANs) have recently attracted considerable
attention in the AI community due to their ability to generate high-quality data of significant …

We consider universal adversarial patches for faces—small visual elements whose addition
to a face image reliably destroys the performance of face detectors. Unlike previous work …

With the rising demand for wireless services and increased awareness of the need for data
protection, existing network traffic analysis and management architectures are facing …

Generative Adversarial Networks (GANs) are a thriving unsupervised machine learning
technique that has led to significant advances in various fields such as computer vision …

Several recently proposed architectures of neural networks such as ResNeXt, Inception,
Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple …

Deep clustering (DC) leverages the representation power of deep architectures to learn
embedding spaces that are optimal for cluster analysis. This approach filters out low-level …

Generative adversarial networks (GANs) have been extensively used for dozens of image
enhancement and image translation applications, where several traditional and novel …

We introduce a distance metric between two distributions and propose a Generative
Adversarial Network (GAN) model: the Simplified Fréchet distance (SFD) and the Simplified …