POT and PPOT
POT is a foundational optimal transport library that PPOT builds upon to implement its progressive partial optimal transport algorithm for clustering applications, making them complements rather than competitors.
About POT
PythonOT/POT
POT : Python Optimal Transport
This library helps data scientists and machine learning engineers analyze how two different datasets or signals can be optimally transformed to match each other. It takes in various types of data distributions (like images, signals, or feature sets) and outputs the most efficient "transport plan" or mapping between them. This is particularly useful for tasks such as comparing different image patterns or adapting models across varied data domains.
About PPOT
rhfeiyang/PPOT
Official implementation of 'P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering'. (Accepted by ICLR 2024)
This tool helps researchers and data scientists improve the accuracy of image classification and recognition when working with datasets where some categories have far fewer examples than others. You input your imbalanced image dataset, and it outputs a more robust clustering model that can better identify objects or features in underrepresented classes. This is ideal for those building computer vision models for diverse real-world scenarios.
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