Domain Mixing

Domain mixing is a technique in machine learning that addresses the challenge of adapting models trained on one data distribution (domain) to perform well on another, often unseen, distribution. Current research focuses on developing algorithms and model architectures that effectively combine or "mix" data from different domains, often using generative models like diffusion models or GANs, to create more robust and generalizable models. This approach is crucial for improving the performance of various applications, including image classification, semantic segmentation, and text-to-speech synthesis, where training and testing data may differ significantly, and for mitigating the effects of spurious correlations in training data.