Optimal Transport for Domain Adaptation through Gaussian Mixture Models
Abstract: Machine learning systems operate under the assumption that training and test data are sampled from a fixed probability distribution. However, this assumptions is rarely verified in practice, as the conditions upon which data was acquired are likely to change. In this context, unsupervised domain adaptation requires minimal access to data from the new conditions for learning models robust to changes in the data distribution. Optimal transport is a theoretically grounded tool for analyzing changes in distribution, especially as it allows the mapping between domains. However, these methods are usually computationally expensive, as their complexity scales cubically with the number of samples. In this work, we explore optimal transport between Gaussian Mixture Models (GMMs), which is conveniently written in terms of the components of source and target GMMs. We experiment with 9 benchmarks, with a total of $85$ adaptation tasks, showing that our methods are more efficient than previous methods, and they scale well with both number of samples and dimensions.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Vincent_Dumoulin1
Submission Number: 3509
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