Partial Distribution Alignment via Adaptive Optimal Transport

As an compare non-parametric probability distributions by exploiting instantiation application, we propose a novel machine learning the geometry of the underlying metric space. To name a few, paradigm based on adaptive optimal transport. It conducts optimal transport plays a crucial role in a wide variety of the partial distribution alignment between source and target machine learning applications, such as generative adversarial domains by treating the noises, outliers, and distribution shifts networks [1], computer vision [2], natural language processing in a principled way. Furthermore, we investigate the mass [3], clustering [4], semi-supervised learning [5], and domain allocation mechanism of adaptive optimal transport and derive adaptation [6]. The essential problem in these applications is the duality theory. The theoretical analysis provides insights how to compare two probability distributions such as aligning into adaptive optimal transport and reinforces its mathematical the fake images with the real images, aligning images with foundation. We believe that adaptive optimal transport is of audio, or aligning the AI generated content with human great interests to the broad areas such as artificial intelligence, feedback in large language model.