Encoding Invariances in Deep Generative Models

06/04/2019 ∙ by Viraj Shah, et al. ∙ 5 ∙ share

Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, training samples obey invariances that are a priori known; for example, in complex physics simulations, the training data obey universal laws encoded as well-defined mathematical equations. In this paper, we propose a new generative modeling approach, InvNet, that can efficiently model data spaces with known invariances. We devise an adversarial training algorithm to encode them into data distribution. We validate our framework in three experimental settings: generating images with fixed motifs; solving nonlinear partial differential equations (PDEs); and reconstructing two-phase microstructures with desired statistical properties. We complement our experiments with several theoretical results.

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Encoding Invariances in Deep Generative Models

Authors

Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks

Physics-informed deep generative models

Stabilizing Training of Generative Adversarial Networks through Regularization

Generative Adversarial Networks

Analyzing and Improving Generative Adversarial Training for Generative Modeling and Out-of-Distribution Detection

Generative adversarial training of product of policies for robust and adaptive movement primitives

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