43 research outputs found
Adversarial Variational Optimization of Non-Differentiable Simulators
Complex computer simulators are increasingly used across fields of science as
generative models tying parameters of an underlying theory to experimental
observations. Inference in this setup is often difficult, as simulators rarely
admit a tractable density or likelihood function. We introduce Adversarial
Variational Optimization (AVO), a likelihood-free inference algorithm for
fitting a non-differentiable generative model incorporating ideas from
generative adversarial networks, variational optimization and empirical Bayes.
We adapt the training procedure of generative adversarial networks by replacing
the differentiable generative network with a domain-specific simulator. We
solve the resulting non-differentiable minimax problem by minimizing
variational upper bounds of the two adversarial objectives. Effectively, the
procedure results in learning a proposal distribution over simulator
parameters, such that the JS divergence between the marginal distribution of
the synthetic data and the empirical distribution of observed data is
minimized. We evaluate and compare the method with simulators producing both
discrete and continuous data.Comment: v4: Final version published at AISTATS 2019; v5: Fixed typo in Eqn 1
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
This paper tackles unpaired image enhancement, a task of learning a mapping
function which transforms input images into enhanced images in the absence of
input-output image pairs. Our method is based on generative adversarial
networks (GANs), but instead of simply generating images with a neural network,
we enhance images utilizing image editing software such as Adobe Photoshop for
the following three benefits: enhanced images have no artifacts, the same
enhancement can be applied to larger images, and the enhancement is
interpretable. To incorporate image editing software into a GAN, we propose a
reinforcement learning framework where the generator works as the agent that
selects the software's parameters and is rewarded when it fools the
discriminator. Our framework can use high-quality non-differentiable filters
present in image editing software, which enables image enhancement with high
performance. We apply the proposed method to two unpaired image enhancement
tasks: photo enhancement and face beautification. Our experimental results
demonstrate that the proposed method achieves better performance, compared to
the performances of the state-of-the-art methods based on unpaired learning.Comment: Accepted to AAAI 202