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Energy-Calibrated VAE with Test Time Free Lunch
April 9, 2024, 4:48 a.m. | Yihong Luo, Siya Qiu, Xingjian Tao, Yujun Cai, Jing Tang
cs.CV updates on arXiv.org arxiv.org
Abstract: In this paper, we propose a novel generative model that utilizes a conditional Energy-Based Model (EBM) for enhancing Variational Autoencoder (VAE), termed Energy-Calibrated VAE (EC-VAE). Specifically, VAEs often suffer from blurry generated samples due to the lack of a tailored training on the samples generated in the generative direction. On the other hand, EBMs can generate high-quality samples but require expensive Markov Chain Monte Carlo (MCMC) sampling. To address these issues, we introduce a conditional …
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