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Found 4 papers, 1 papers with code
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness
Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations.
Distilling Adversarial Robustness Using Heterogeneous Teachers
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e. g., self-driving cars or medical imaging.
Auto-Encoding Goodness of Fit
Goodness of fit (GoF) hypothesis tests provide a measure of statistical indistinguishability between the latent distribution and a target distribution class.
VIGAN: Missing View Imputation with Generative Adversarial Networks
Especially, when certain samples miss an entire view of data, it creates the missing view problem.
