machinestein/General-Cost-Neural-Optimal-Transport
Official Pytorch implementation of "Neural Optimal Transport with General Cost Functionals" (ICLR 2024)
This project helps researchers and practitioners in machine learning and data science who need to transform data distributions from one form to another while minimizing a specific 'cost' or 'effort' for that transformation. It takes in two data distributions and a custom cost function, then outputs a neural network that maps one distribution to the other efficiently. This is ideal for scientists working with complex datasets where traditional data alignment methods fall short.
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Use this if you need to align or translate complex data distributions (like images or biological data) based on a custom, high-dimensional cost function that reflects the specific transformation you want.
Not ideal if your data transformations are simple, low-dimensional, or if you don't need to define a custom, complex cost function for the transport.
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Aug 29, 2024
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