<ul data-eligibleForWebStory="false"><li>Machine learning systems often assume training and test data come from the same distribution, but this is rarely the case in real-world scenarios where data conditions may change.</li><li>Adapting unsupervised domains with minimal access to new data is crucial for building models robust to distribution changes.</li><li>This study explores optimal transport between Gaussian Mixture Models (GMMs) for analyzing distribution changes efficiently, showing promising results in various benchmarks.</li><li>The proposed method is more efficient and scalable compared to previous shallow domain adaptation methods, performing well with varying sample sizes and dimensions.</li></ul>

Optimal Transport for Domain Adaptation through Gaussian Mixture Models

Discover more