Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Image-based virtual try-on systems for fitting new in-shop clothes into a person image have
attracted increasing research attention, yet is still challenging. A desirable pipeline should …

Deep learning methods have shown promise in unsupervised domain adaptation, which
aims to leverage a labeled source domain to learn a classifier for the unlabeled target …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

Despite remarkable advances in image synthesis research, existing works often fail in
manipulating images under the context of large geometric transformations. Synthesizing …

Deep generative models are becoming a cornerstone of modern machine learning. Recent
work on conditional generative adversarial networks has shown that learning complex, high …

Characterizing statistical properties of solutions of inverse problems is essential for decision
making. Bayesian inversion offers a tractable framework for this purpose, but current …

Clustering is an important problem in various machine learning applications, but still a
challenging task when dealing with complex real data. The existing clustering algorithms …

As a promising area in artificial intelligence, a new learning paradigm, called Small Sample
Learning (SSL), has been attracting prominent research attention in the recent years. In this …