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Found 6 papers, 3 papers with code
Theoretical Understanding of Learning from Adversarial Perturbations
In this study, we provide a theoretical framework for understanding learning from perturbations using a one-hidden-layer network trained on mutually orthogonal samples.
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
Furthermore, the proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation
In order to improve the robustness against out-of-distribution data, we present a frequency-based data augmentation technique that replaces the frequency components with other images of the same class.
Prediction of Seismic Intensity Distributions Using Neural Networks
The ground motion prediction equation is commonly used to predict the seismic intensity distribution.
Superclass Adversarial Attack
Adversarial attacks have only focused on changing the predictions of the classifier, but their danger greatly depends on how the class is mistaken.
Are DNNs fooled by extremely unrecognizable images?
In this paper, we address the question of whether there can be fooling images with no characteristic pattern of natural objects locally or globally.
