Robust Perception through Equivariance

Understand

Deep networks for computer vision are not reliable when they encounter adversarial examples.

• In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference.
• By introducing constraints at inference time, we can shift the burden of robustness from training to the inference algorithm, thereby allowing the model to adjust dynamically to each individual image's unique and potentially novel characteristics at inference time.
• Among different constraints, we find that equivariance-based constraints are most effective, because they allow dense constraints in the feature space without overly constraining the representation at a fine-grained level.