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arXiv · 2603.26657

Tunable Soft Equivariance with Guarantees

Md Ashiqur Rahman·Lim Jun Hao·Jeremiah Jiang·Teck-Yian Lim·Raymond A. Yeh
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Abstract

Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling the degree of equivariance is therefore desirable. We propose a general framework for constructing soft equivariant models by projecting the model weights into a designed subspace. The method applies to any pre-trained architecture and provides theoretical bounds on the induced equivariance error. Empirically, we demonstrate the effectiveness of our method on multiple pre-trained backbones, including ViT and ResNet, across image classification, semantic segmentation, and human-trajectory prediction tasks. Notably, our approach improves the performance while simultaneously reducing equivariance error on the competitive ImageNet benchmark.

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Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang, Teck-Yian Lim, Raymond A. Yeh. 2026-03-27. Tunable Soft Equivariance with Guarantees. https://arxiv.org/abs/2603.26657

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