Copula & Marginal Flows: Disentangling the Marginal from its Joint

Abstract

Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generated by a generative network is not available. In this paper, we address this issue for the first time in the deep learning literature by making two novel contributions. First, we derive upper bounds for the tails that can be expressed by a generative network and demonstrate Lp-space related properties. There we show specifically that in various situations an optimal generative network does not exist. Second, we introduce and propose copula and marginal generative flows (CM flows) which allow for an exact modeling of the tail and any prior assumption on the CDF up to an approximation of the uniform distribution. Our numerical results support the use of CM flows.

References in corpus (3)

• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Variational Inference with Normalizing Flows
• Density estimation using Real NVP

Cited by in corpus (6)

• Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation
• Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
• Copula-Based Normalizing Flows
• On some theoretical limitations of Generative Adversarial Networks
• Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
• Generative Archimedean Copulas