mjalali/renyi-kernel-entropy
[NeurIPS 2023] Code base for the Renyi Kernel Entropy (RKE) metric for generative models.
This tool helps machine learning researchers and practitioners rigorously evaluate how well their generative models capture the full diversity of real-world data, especially when that data has many distinct 'modes' or clusters, such as different categories of images. You input features extracted from both real data and your model's generated data, and it outputs a score that quantifies the number of modes your model has successfully learned and generated. It's designed for researchers developing or comparing new generative AI models.
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Use this if you are a machine learning researcher or engineer developing generative AI models and need a reliable metric to quantify how many distinct data patterns your models produce compared to real-world data.
Not ideal if you are primarily interested in the visual quality or fidelity of generated samples, or if your data does not have a clearly multi-modal structure.
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Python
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MIT
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Last pushed
Jun 18, 2025
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