While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the label spaces of the two domains are not congruent. To improve the transferability of the trained models, particularly in setups where only the healthy data class is ...
Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely...
domain adaptation; open-set setting; adversarial neural networks1. Introduction Supervised learning techniques work under the assumption that training and testing datasets are drawn from the same distribution. Thus, traditional techniques require at least some labeled data for the problem at hand so as...
Partial Adversarial Domain Adaptation Zhangjie Cao, Lijia Ma, Mingsheng Long( ), and Jianmin Wang School of Software, Tsinghua University, China National Engineering Laboratory for Big Data Software Beijing National Research Center for Information Science and Technology {caozhangjie14,malijia15}@gmail....
Nanyang Technological University, Singapore 2Institute for Infocomm Research, A*STAR, Singapore Abstract Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source ...
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Scientific Reports | Vol:.(1234567890) (2023) 13:21723 | https://doi.org/10.1038/s41598-023-49100-6 14 www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution ...