Generator Reversal
Yannic Kilcher, Aur\'elien Lucchi, Thomas Hofmann

TL;DR
This paper introduces a novel approach to training generative models by reversing the generator to estimate flexible code distributions, leading to more powerful models and improved latent structure modeling.
Contribution
It proposes a non-parametric method to estimate code distributions by reversing the generator, enhancing model flexibility and control over generalization.
Findings
More powerful generative models achieved
Improved modeling of latent structure
Explicit control of generalization degree
Abstract
We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. These distributions are estimated non-parametrically by reversing the generator map during training. The benefits include: more powerful generative models, better modeling of latent structure and explicit control of the degree of generalization.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Machine Learning and Algorithms · Domain Adaptation and Few-Shot Learning
