Tunable Soft Equivariance with Guarantees

• URL: http://arxiv.org/abs/2603.26657v1
• Date: Fri, 27 Mar 2026 17:56:25 GMT
• Title: Tunable Soft Equivariance with Guarantees
• Abstract summary: We propose a framework for constructing soft equivariant models by projecting the model weights into a designed subspace.<n>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.
• Score: 23.430388335509463
• License: http://creativecommons.org/licenses/by/4.0/
• 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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