Persistently Trained, Diffusion-assisted Energy-based Models

• URL: http://arxiv.org/abs/2304.10707v1
• Date: Fri, 21 Apr 2023 02:29:18 GMT
• Title: Persistently Trained, Diffusion-assisted Energy-based Models
• Authors: Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
• Abstract summary: We introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training. We show that persistently trained EBMs can simultaneously achieve long-run stability, post-training image generation, and superior out-of-distribution detection.
• Score: 18.135784288023928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training (i.e., using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that, for the first time for image data, persistently trained EBMs can {\it simultaneously} achieve long-run stability, post-training image generation, and superior out-of-distribution detection.

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