Latent Equivariant Operators for Robust Object Recognition: Promise and Challenges

• URL: http://arxiv.org/abs/2602.18406v2
• Date: Mon, 23 Feb 2026 18:44:49 GMT
• Title: Latent Equivariant Operators for Robust Object Recognition: Promise and Challenges
• Authors: Minh Dinh, Stéphane Deny,
• Abstract summary: Equivariant networks are a solution to the problem of generalizing knowledge across symmetric transformations.<n>Here, we show how we can successfully harness these networks for simple out-of-distribution classification.<n>While conceptually enticing, we discuss challenges ahead on the path to more complex datasets.
• Score: 2.1198879079315573
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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