Gradient Estimators for Implicit ModelsDownload PDF

15 Feb 2018 (modified: 16 Jun 2024)ICLR 2018 Conference Blind SubmissionReaders: Everyone
Abstract: Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradient-based optimisation, which is liable to produce inaccurate updates and thus poor models. This paper alleviates the need for such approximations by proposing the \emph{Stein gradient estimator}, which directly estimates the score function of the implicitly defined distribution. The efficacy of the proposed estimator is empirically demonstrated by examples that include meta-learning for approximate inference and entropy regularised GANs that provide improved sample diversity.
TL;DR: We introduced a novel gradient estimator using Stein's method, and compared with other methods on learning implicit models for approximate inference and image generation.
Keywords: Implicit Models, Approximate Inference, Deep Learning
Code: [![github](/images/github_icon.svg) YingzhenLi/SteinGrad](
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](
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