Latent Equivariant Operators for Robust Object Recognition: Promises and Challenges

TL;DR

This paper explores latent space equivariant operators for robust object recognition, demonstrating their effectiveness on simple datasets and discussing challenges in scaling to complex data.

Contribution

It introduces a method for learning equivariant operators in latent space from examples, offering an alternative to traditional and fixed equivariant neural networks.

Findings

Successfully classified rotated and translated noisy MNIST in out-of-distribution settings

Outperformed traditional and equivariant networks on simple datasets

Discussed challenges in scaling to more complex datasets

Abstract

Despite the successes of deep learning in computer vision, difficulties persist in recognizing objects that have undergone group-symmetric transformations rarely seen during training$\unicode{x2013}$for example objects seen in unusual poses, scales, positions, or combinations thereof. Equivariant neural networks are a solution to the problem of generalizing across symmetric transformations, but require knowledge of transformations a priori. An alternative family of architectures proposes to learn equivariant operators in a latent space, from examples of symmetric transformations. Here, using simple datasets of rotated and translated noisy MNIST, we illustrate how such architectures can successfully be harnessed for out-of-distribution classification, thus overcoming the limitations of both traditional and equivariant networks. While conceptually enticing, we discuss challenges ahead on the path of scaling these architectures to more complex datasets. Our code is available at https://github.com/BRAIN-Aalto/equivariant_operator.