55 citations · 101 across the 7 of their papers we have counts for
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stat.ML2020★ 20 cited
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent
Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen
Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial tr…
eess.IV2020★ 3 cited
Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields
Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet +1
The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This informati…