2017/06/23 by Hemanth Venkateswara, Venkateswara, Hemanth, Shayok Chakraborty +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Cancer-related molecular mechanisms research #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Multimodal Machine Learning Applications #cs.AI
paper · pdf · doi:10.48550/arxiv.1706.07527
AAAI Workshops 2017
arxiv created 2017/06/23 · openalex publication_date 2017/06/23 · arxiv updated 2017/06/26 · openalex created_date 2017/06/30 · openalex updated_date 2026/07/28
Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain-aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.