PangzeCheung/Discrete-Probability-Flow
[NeurIPS 2023] Formulating Discrete Probability Flow Through Optimal Transport
This project offers a method for generating clear and well-defined images and other discrete data. It takes in a dataset (like images or synthetic patterns) and can produce new, high-quality samples or smoothly blend between existing ones. Image and data scientists working with generative models who need more precise and controllable outputs would find this useful.
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Use this if you are developing or experimenting with generative models and need a method to produce more certain and controllable discrete data samples, especially for tasks like image generation or latent space interpolation.
Not ideal if you are looking for an out-of-the-box application for general image editing or content creation, as this is a foundational research tool for generative model development.
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Python
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Last pushed
Jan 08, 2024
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