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Found 5 papers, 1 papers with code
Understanding Noise-Augmented Training for Randomized Smoothing
This method relies on taking a majority vote of any base classifier over multiple noise-perturbed inputs to obtain a smoothed classifier, and it remains the tool of choice to certify deep and complex neural network models.
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection
However, obtaining a large amount of training annotations specific to a particular task, i. e., fine-grained annotations, is costly in practice.
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new attacks are proposed that break earlier defenses, and so on.
On the Regularization Properties of Structured Dropout
We also show that the global minimizer for DropBlock can be computed in closed form, and that DropConnect is equivalent to Dropout.
An Empirical Evaluation of Visual Question Answering for Novel Objects
We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data.
