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Found 4 papers, 2 papers with code
Certified Patch Robustness via Smoothed Vision Transformers
Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region.
Leveraging Sparse Linear Layers for Debuggable Deep Networks
We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks.
Adversarially Robust Generalization Requires More Data
We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.
A Classification-Based Study of Covariate Shift in GAN Distributions
A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on.
