A Method to Model Conditional Distributions with Normalizing Flows
arXiv:1911.02052
Abstract
In this work, we investigate the use of normalizing flows to model conditional distributions. In particular, we use our proposed method to analyze inverse problems with invertible neural networks by maximizing the posterior likelihood. Our method uses only a single loss and is easy to train. This is an improvement on the previous method that solves similar inverse problems with invertible neural networks but which involves a combination of several loss terms with ad-hoc weighting. In addition, our method provides a natural framework to incorporate conditioning in normalizing flows, and therefore, we can train an invertible network to perform conditional generation. We analyze our method and perform a careful comparison with previous approaches. Simple experiments show the effectiveness of our method, and more comprehensive experimental evaluations are undergoing.
10 pages. Work in progress
References in corpus (10)
- Auto-Encoding Variational Bayes
- Conditional Generative Adversarial Nets
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- NICE: Non-linear Independent Components Estimation
- Density estimation using Real NVP
- Guided Image Generation with Conditional Invertible Neural Networks
- Analyzing Inverse Problems with Invertible Neural Networks
- Comparison of Maximum Likelihood and GAN-based training of Real NVPs
- Generative Latent Flow