Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop unseen test images, due to various domain discrepancy between the training (source domain) and (target domain), which raises a adaptation issue. In clinical settings, it is difficult collect enough annotated target data in short period. Few-shot adaptation, i.e., adapting model with handful...

Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed achieve transferrable models. Among them, most prevalent method is adversarial adaptation, which can shorten distance between source and target domain. Although learning very effective, it still leads instability of network drawbacks confusing category information. In this paper,...

In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance deg...

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to th...

In this paper, we provide a new framework to study the generalization bound of the learning process for domain adaptation. We consider two kinds of representative domain adaptation settings: one is domain adaptation with multiple sources and the other is domain adaptation combining source and target data. In particular, we use the integral probability metric to measure the difference between tw...

Domain adaptation problems arise in a variety of applications, where a training dataset from the source domain and a test dataset from the target domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive an...

Domain adaptation problem arises in a variety of applications where the training set (source domain) and testing set (target domain) follow different distributions. The difficulty of such learning problem lies in how to bridge the gap between the source distribution and target distribution. In this paper, we give an formal analysis of feature learning algorithms for domain adaptation with linea...

The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation lear...

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant [20] of temporal ensembling [8], a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness...