DAugNet: Unsupervised, Multisource, Multitarget, and Life-Long Domain Adaptation for Semantic Segmentation of Satellite Images

Unsupervised Domain Adaptation for Semantic Segmentation with GANs

Visual Domain Adaptation is a problem of immense importance in computer vision. Previous approaches showcase the inability of even deep neural networks to learn informative representations across domain shift. This problem is more severe for tasks where acquiring hand labeled data is extremely hard and tedious. In this work, we focus on adapting the representations learned by segmentation netwo...

Algorithm and Unsupervised Segmentation of Satellite Images

Multi Resolution Fuzzy Segmentation of Satellite Images

Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging

Sophisticated models have been developed for joint word segmentation and part-of-speech tagging, with increasing accuracies reported on the Chinese Treebank data. These systems, which rely on supervised learning, typically perform worse on texts from a different domain, for which little annotation is available. We consider self-training and character clustering for domain adaptation. Both metho...

Discovering Latent Domains for Multisource Domain Adaptation

Recent domain adaptation methods successfully learn crossdomain transforms to map points between source and target domains. Yet, these methods are either restricted to a single training domain, or assume that the separation into source domains is known a priori. However, most available training data contains multiple unknown domains. In this paper, we present both a novel domain transform mixtu...