This paper aims to interpret how deepfake detection models learn artifact features of images
when just supervised by binary labels. To this end, three hypotheses from the perspective of …

Recently, a series of studies have tried to extract interactions between input variables
modeled by a DNN and define such interactions as concepts encoded by the DNN …

In this paper, we use the interaction inside adversarial perturbations to explain and boost the
adversarial transferability. We discover and prove the negative correlation between the …

Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style
architectures have triggered the resurgence of ConvNets. In this work, we explore the …

This paper theoretically explains the intuition that simple concepts are more likely to be
learned by deep neural networks (DNNs) than complex concepts. In fact, recent studies …

This paper introduces the system of game-theoretic interactions, which connects both the
explanation of knowledge encoded in a deep neural networks (DNN) and the explanation of …

This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate
interactions. In this paper, we define and quantify the significance of interactions among …

Masked image modeling, an emerging self-supervised pre-training method, has shown
impressive success across numerous downstream vision tasks with Vision transformers. Its …

By contextualizing the kernel as global as possible, Modern ConvNets have shown great
potential in computer vision tasks. However, recent progress on\textit {multi-order game …

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for
clinical applications. Deep convolutional neural network methods such as DeepLabv3+ …