When Unsupervised Domain Adaptation Meets Tensor Representations Supplementary Materials∗

Deep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning

Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...

Supplementary Material – PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories

This Supplement contains additional materials related to the paper PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories. In particular, in Sec. 2 we present additional results that qualitatively demonstrate the advantages of our adaptation framework over competing approaches such as the ILS [2]. We contrast 2D embeddings of the features adapted by ...

Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed to compensate for the degradation in performance due to domain shift. In this paper, we address the case when the target domain is unlabeled, requiring unsupe...

Online Updating of Word Representations for Part-of-Speech Tagging

We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS) tagging evaluation, we find that online unsupervised DA performs as well as batch DA.

Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer ’ s Disease ( Supplementary Material ) Hao