Tunable Soft Equivariance with Guarantees
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
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Lim Jun Hao
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Jeremiah Jiang
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Teck-Yian Lim
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Raymond A. Yeh
Tunable Soft Equivariance with Guarantees
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.
A general framework for constructing soft equivariant models through weight projection into designed subspaces is proposed, demonstrating improved performance and reduced equivariance error across multiple computer vision tasks.
equivariance soft equivariant models weight projection designed subspace pre-trained architecture theoretical bounds ImageNet image classification semantic segmentation human-trajectory prediction