In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

Following the success in advancing natural language processing and understanding,
transformers are expected to bring revolutionary changes to computer vision. This work …

Deep learning has presented remarkable progress in various tasks. Despite the excellent
performance, deep learning models remain not robust, especially to well-designed …

Since out-of-distribution generalization is a generally ill-posed problem, various proxy
targets (eg, calibration, adversarial robustness, algorithmic corruptions, invariance across …

Current state-of-the-art object recognition models are largely based on convolutional neural
network (CNN) architectures, which are loosely inspired by the primate visual system …

Convolutional Neural Networks (CNNs) have become the de facto gold standard in
computer vision applications in the past years. Recently, however, new model architectures …

Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent
years, with many successful variants such as MixUp and CutMix. By studying the mutual …

Abstract Machine learning classifiers are critically prone to evasion attacks. Adversarial
examples are slightly modified inputs that are then misclassified, while remaining …