Learning domain-adaptive features is important to tackle the dataset bias problem, where
data distributions in the labeled source domain and the unlabeled target domain can be …

Adversarial learning strategy has demonstrated remarkable performance in dealing with
single-source Domain Adaptation (DA) problems, and it has recently been applied to Multi …

Accurate identification of multimodal Superheat Degree (SD) plays a critical decision-
making role in Aluminum Electrolysis Process (AEP). Because the labeled SD data are …

Given data from diverse sets of distinct distributions, domain generalization aims to learn
models that generalize to unseen distributions. A common approach is designing a data …

This survey is for the remembrance of one of the creators of the information bottleneck
theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information …

Learning in nonstationary environments is one of the biggest challenges in machine
learning. Nonstationarity can be caused by either task drift, ie, the drift in the conditional …

Animals are able to imitate each others' behavior, despite their difference in biomechanics.
In contrast, imitating other similar robots is a much more challenging task in robotics. This …

With the development of the distributed learning and edge computing, servers must often
receive information from multiple terminal devices; thus, the importance of source-channel …

The motivation of our research is to explore the possibilities of automatic sound-to-image
(S2I) translation for enabling a human receiver to visually infer occurrences of sound-related …

Both image denoising and watermark removal aim to restore a clean image from an
observed noisy or watermarked one. The past research consists of the non-learning type …