{"repo":"mjalali/renyi-kernel-entropy","free":true,"listed":false,"github":"https://github.com/mjalali/renyi-kernel-entropy","clone":"git clone https://github.com/mjalali/renyi-kernel-entropy.git","description":"[NeurIPS 2023] Code base for the Renyi Kernel Entropy (RKE) metric for generative models.","language":"Python","stars":14,"topics":["diffusion-models","diversity","evaluation","evaluation-metrics","entropy","information-theory","large-language-models","machine-learning","metrics","quantum"],"license":"MIT","category":"machine-learning","readme_excerpt":"RKE score Paper: An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions Mohammad Jalai 1 , Cheuk Ting Li 2 , Farzan Farnia 2 1 Isfahan University of Technology (IUT) , 2 The Chinese University of Hong Kong (CUHK) Work done during an internship at CUHK 1. Background Abstract The evaluation of generative models has received significant attention in the machine learning community. When applied to a multi-modal distribution which is common among image datasets, an intuitive evaluation criterion is the number of modes captured by the generative model. While several scores have been proposed to evaluate the quality and diversity of a model's generated data, the correspondence between existing scores and the number of modes in the distribution is unclear. In this work, we propose an information-theoretic diversity evaluation method for multi-modal underlying distributions. We define the R\\'enyi Kernel Entropy (RKE) as an evaluation score based on quantum information theory to measure the number of modes in generated samples. To interpret the proposed evaluation method, we show that the RKE score can output the number of modes of a mixture of sub-Gaussian components. We also prove estimation error bounds for estimating the RKE score from limited data, suggesting a fast convergence of the empirical RKE score to the score for the underlying data distribution. Utilizing the RKE score, we conduct an extensive evaluation of state-of-the-art generati","default_branch":null,"files":null,"tree":[],"storefront":"/r/mjalali","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/mjalali/renyi-kernel-entropy/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}