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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