Multiscale Generative Models: Improving Performance of a Generative
Model Using Feedback from Other Dependent Generative Models
- URL: http://arxiv.org/abs/2201.09644v1
- Date: Mon, 24 Jan 2022 13:05:56 GMT
- Title: Multiscale Generative Models: Improving Performance of a Generative
Model Using Feedback from Other Dependent Generative Models
- Authors: Changyu Chen, Avinandan Bose, Shih-Fen Cheng, Arunesh Sinha
- Abstract summary: We take a first step towards building interacting generative models (GANs) that reflects the interaction in real world.
We build and analyze a hierarchical set-up where a higher-level GAN is conditioned on the output of multiple lower-level GANs.
We present a technique of using feedback from the higher-level GAN to improve performance of lower-level GANs.
- Score: 10.053377705165786
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Realistic fine-grained multi-agent simulation of real-world complex systems
is crucial for many downstream tasks such as reinforcement learning. Recent
work has used generative models (GANs in particular) for providing
high-fidelity simulation of real-world systems. However, such generative models
are often monolithic and miss out on modeling the interaction in multi-agent
systems. In this work, we take a first step towards building multiple
interacting generative models (GANs) that reflects the interaction in real
world. We build and analyze a hierarchical set-up where a higher-level GAN is
conditioned on the output of multiple lower-level GANs. We present a technique
of using feedback from the higher-level GAN to improve performance of
lower-level GANs. We mathematically characterize the conditions under which our
technique is impactful, including understanding the transfer learning nature of
our set-up. We present three distinct experiments on synthetic data, time
series data, and image domain, revealing the wide applicability of our
technique.
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