Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Recent advances in domain adaptation show that deep self-training presents a powerful
means for unsupervised domain adaptation. These methods often involve an iterative …

Generalizing a deep learning model to new domains is crucial for computer-aided medical
diagnosis systems. Most existing unsupervised domain adaptation methods have made …

Existing approaches to depth or disparity estimation output a distribution over a set of pre-
defined discrete values. This leads to inaccurate results when the true depth or disparity …

Congenital heart disease (CHD) is the most common birth defect and the leading cause of
neonate death in China. Clinical diagnosis can be based on the selected 2D key-frames …

AI Safety is a major concern in many deep learning applications such as autonomous
driving. Given a trained deep learning model, an important natural problem is how to reliably …

We present a new learning-based framework to recover vehicle pose in SO (3) from a single
RGB image. In contrast to previous works that map local appearance to observation angles …

This paper targets for the ordinal regression/classification, which objective is to learn a rule
to predict labels from a discrete but ordered set. For instance, the classification for medical …

Deep neural networks are usually data-starved, but manually annotation can be costly in
many specific tasks. For instance, the emotion recognition from the audio. However, there is …

Semantic segmentation (SS) is an important perception manner for self-driving cars and
robotics, which classifies each pixel into a pre-determined class. The widely-used cross …