Large text-to-image diffusion models have impressive capabilities in generating
photorealistic images from text prompts. How to effectively guide or control these powerful …

We present a new direction for increasing the interpretability of deep neural networks
(DNNs) by promoting weight-input alignment during training. For this, we propose to replace …

Neural networks are a powerful class of nonlinear functions that can be trained end-to-end
on various applications. While the over-parametrization nature in many neural networks …

This paper addresses the deep face recognition problem under an open-set protocol, where
ideal face features are expected to have smaller maximal intra-class distance than minimal …

One of the most prominent attributes of Neural Networks (NNs) constitutes their capability of
learning to extract robust and descriptive features from high dimensional data, like images …

Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in
many computer vision tasks. However, little effort has been devoted to establishing …

Although deep learning-based face recognition techniques have achieved amazing
performance in recent years, low-resolution (LR) face recognition remains challenging. In …

This paper introduces hyperspherical prototype networks, which unify classification and
regression with prototypes on hyperspherical output spaces. For classification, a common …

Maximizing the separation between classes constitutes a well-known inductive bias in
machine learning and a pillar of many traditional algorithms. By default, deep networks are …

Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear
function approximation. In order to achieve good generalization on unseen data, a suitable …