Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

In view of the faultiness that the existing image inpainting methods fail to make full use of the
complete region to predict the missing region features when the object features are seriously …

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives
inspiration primarily from deep learning approaches, achieves state-of-the-art performance …

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an
active topic in computer vision but lacks a theoretical foundation. We first extend existing …

In this paper, we study two challenges in clustering analysis, namely, how to cluster multi-
view data and how to perform clustering without parameter selection on cluster size. To this …

In the last few years, image inpainting methods based on deep learning models had shown
obvious advantages compared with existing traditional methods. The former can better …

The most image inpainting algorithms often have existed problems such as blurred image,
texture distortion and semantic inaccuracy, and the image inpainting effect is limited for …

Popular deep-learning models in the field of image classification suffer from catastrophic
forgetting—models will forget previously acquired skills when learning new ones …

Abstract Interestingly, recent experimental results [2, 26, 22] have identified a robust fairness
phenomenon in adversarial training (AT), namely that a robust model well-trained by AT …