Contrastive Implicit Representation Learning

TL;DR — We perform SimCLR on implicit neural representations.

Abstract

Implicit Neural Representations have emerged as an interesting alternative to traditional array representations. The challenge of performing downstream tasks directly on implicit representations has been addressed by several methods. Overcoming this challenge would open the door to the application of implicit representations to a wide range of fields. Then again, self-supervised representation learning methods, such as the several contrastive learning frameworks which have been proven powerful representation learning methods. So far, the use of self-supervised learning for implicit representations has remained unexplored, mostly because of the difficulty of producing valid augmented views of implicit representations to be used for learning contrasts. In this work, we adapt the popular SimCLR algorithm to implicit representations that consist of multiplicative filters networks and SIRENs. While methods to obtain augmentations in SIREN have been studied in the literature, we provide methods for augmenting MFNs effectively. We show how MFNs lend themselves well to geometric augmentations. To the best of our knowledge, our work is the first to demonstrate that self-supervised learning on implicit representations of images is feasible and results in good downstream task performances.