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End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
Shohei Taniguchi · Masahiro Suzuki · Yusuke Iwasawa · Yutaka Matsuo

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #431

We address the problem of biased gradient estimation in deep Boltzmann machines (DBMs). The existing method to obtain an unbiased estimator uses a maximal coupling based on a Gibbs sampler, but when the state is high-dimensional, it takes a long time to converge. In this study, we propose to use a coupling based on the Metropolis-Hastings (MH) and to initialize the state around a local mode of the target distribution. Because of the propensity of MH to reject proposals, the coupling tends to converge in only one step with a high probability, leading to high efficiency. We find that our method allows DBMs to be trained in an end-to-end fashion without greedy pretraining. We also propose some practical techniques to further improve the performance of DBMs. We empirically demonstrate that our training algorithm enables DBMs to show comparable generative performance to other deep generative models, achieving the FID score of 10.33 for MNIST.

Author Information

Shohei Taniguchi (The University of Tokyo)
Masahiro Suzuki (The University of Tokyo, Tokyo Institute of Technology)
Yusuke Iwasawa (The University of Tokyo)
Yutaka Matsuo (University of Tokyo)

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