Meta Internal Learning

Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf

Keywords: hypernetworks, meta learning, single image generation, gan, computer vision

Abstract:

Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively.
In the presented meta-learning approach, a single-image GAN model is generated given an input image, via a convolutional feedforward hypernetwork $f$. This network is trained over a dataset of images, allowing for feature sharing among different models, and for interpolation in the space of generative models. The generated single-image model contains a hierarchy of multiple generators and discriminators. It is therefore required to train the meta-learner in an adversarial manner, which requires careful design choices that we justify by a theoretical analysis. Our results show that the models obtained are as suitable as single-image GANs for many common image applications, {significantly reduce the training time per image without loss in performance}, and introduce novel capabilities, such as interpolation and feedforward modeling of novel images.

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Code: https://github.com/RaphaelBensTAU/MetaInternalLearning

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2110.02900/code)