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Learning Energy-Based Models With Adversarial Training
[article]
2022
arXiv
pre-print
We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT). We show that (binary) AT learns a special kind of energy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating diverse and realistic images. Aside from having
arXiv:2012.06568v4
fatcat:ynmnrpdu4vefxjez2to2ygtsjq