# Mind2Mind : transfer learning for GANs

## TL;DR

This paper introduces a transfer learning method for GANs that significantly accelerates training by freezing low-level layers, supported by theoretical convergence guarantees within the optimal transport framework.

## Contribution

It proposes a novel transfer learning technique for GANs that reduces training time and provides rigorous convergence analysis with bounds based on dataset similarity.

## Key findings

- Achieves up to 100x faster training on high-quality datasets compared to StyleGAN.
- Theoretical proof of convergence within the optimal transport framework.
- Provides bounds on training convergence related to dataset differences.

## Abstract

Training generative adversarial networks (GANs) on high quality (HQ) images involves important computing resources. This requirement represents a bottleneck for the development of applications of GANs. We propose a transfer learning technique for GANs that significantly reduces training time. Our approach consists of freezing the low-level layers of both the critic and generator of the original GAN. We assume an autoencoder constraint in order to ensure the compatibility of the internal representations of the critic and the generator. This assumption explains the gain in training time as it enables us to bypass the low-level layers during the forward and backward passes. We compare our method to baselines and observe a significant acceleration of the training. It can reach two orders of magnitude on HQ datasets when compared with StyleGAN. We prove rigorously, within the framework of optimal transport, a theorem ensuring the convergence of the learning of the transferred GAN. We moreover provide a precise bound for the convergence of the training in terms of the distance between the source and target dataset.