[Paper] Tunable Soft Equivariance with Guarantees

Summary

This paper proposes a general framework for constructing "soft equivariant" models in computer vision, addressing the issue that strict equivariance is rarely satisfied in real-world data. The method involves projecting model weights into a designed subspace, allowing control over the degree of equivariance. It applies to any pre-trained architecture and provides theoretical guarantees on the induced equivariance error, aiming to improve model performance and robustness.

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