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Energy-based models parameterize the unnormalized log-probability of datasamples, but there is a lack of guidance on how to construct the "energy". Inthis paper, we propose a Denoising-EBM which decomposes the image energy into"semantic energy" and "texture energy". We define the "semantic energy" in thelatent space of DAE to model the high-level representations, and define thepixel-level reconstruction error for denoising as "texture energy". Inspired byscore-based model, our model utilizes multi-scale noisy samples formaximum-likelihood training and it outputs a vector instead of a scalar forexploring a larger set of functions during optimization. After training, thesemantics are first synthesized by fast MCMC through "semantic energy", andthen the pixel-level refinement of semantic image will be performed to generateperfect samples based on "texture energy". Ultimately, our model can outperformmost EBMs in image generation. And we also demonstrate that Denoising-EBM hastop performance among EBMs for out-of-distribution detection.

How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model