Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

Public resources and services (eg, datasets, training platforms, pre-trained models) have
been widely adopted to ease the development of Deep Learning-based applications …

It is commonly believed that networks cannot be both accurate and robust, that gaining
robustness means losing accuracy. It is also generally believed that, unless making …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

Deep neural networks are vulnerable to adversarial attacks, in which imperceptible
perturbations to their input lead to erroneous network predictions. This phenomenon has …

Most existing defenses against adversarial attacks only consider robustness to L_p-
bounded distortions. In reality, the specific attack is rarely known in advance and …

It has been widely recognized that adversarial examples can be easily crafted to fool deep
networks, which mainly root from the locally non-linear behavior nearby input examples …