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Found 5 papers, 3 papers with code
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Semi-supervised learning (SSL) algorithm is a setup built upon a realistic assumption that access to a large amount of labeled data is tough.
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation
For example, our algorithm with only 8\% labeled data is comparable to supervised adversarial training algorithms that use all labeled data, both in terms of standard and robust accuracies on CIFAR-10.
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications.
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of data to deceive a given deep neural network.
Masked Bayesian Neural Networks : Computation and Optimality
As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify such complex and huge DNNs.
