A new pathway to generative artificial intelligence by minimizing the maximum entropy

TL;DR

This paper introduces a novel framework for generative AI that minimizes maximum entropy to create more data-efficient, controllable, and unbiased models, outperforming existing autoencoders especially with limited data.

Contribution

It proposes a physics-driven, entropy-based paradigm shift that enhances data efficiency and controllability in generative models, surpassing variational autoencoders.

Findings

Outperforms variational autoencoders in benchmarks

Effective in generating images with limited data

Enables post-hoc customization without retraining

Abstract

Generative artificial intelligence revolutionized society. Current models are trained by minimizing the distance between the produced data and the training set. Consequently, development is plateauing as they are intrinsically data-hungry and challenging to direct during the generative process. To overcome these limitations, we introduce a paradigm shift through a framework where we do not fit the training set but find the most informative yet least noisy representation of the data simultaneously minimizing the entropy to reduce noise and maximizing it to remain unbiased via adversary training. The result is a general physics-driven model, which is data-efficient and flexible, permitting to control and influence the generative process. Benchmarking shows that our approach outperforms variational autoencoders. We demonstrate the methods effectiveness in generating images, even with limited training data, and its unprecedented capability to customize the generation process a posteriori without any fine-tuning or retraining