论文标题
多尺度生成模型：使用其他因生成模型的反馈来改善生成模型的性能
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models
论文作者
论文摘要
对现实世界复杂系统的现实细粒度模拟对许多下游任务，例如加固学习至关重要。最近的工作使用了生成模型（特别是gan）来提供对现实世界系统的高保真模拟。但是，这种生成模型通常是单片的，并且错过了对多代理系统中的相互作用进行建模。在这项工作中，我们朝着建立反映现实世界中相互作用的多种相互作用的生成模型（GAN）迈出的第一步。我们构建和分析了一个分层设置，其中高级gan的条件是基于多个低级gan的输出。我们提出了一种使用高级GAN的反馈来提高低级gan的性能的技术。我们在数学上表征了我们技术有影响力的条件，包括了解设置的转移学习性质。我们介绍了有关合成数据，时间序列数据和图像域的三个不同的实验，揭示了我们技术的广泛适用性。
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.
