1 citations · 2 across the 10 of their papers we have counts for
3 papers · 1 filter
Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation
Koki Mukai, Soichiro Kumano, Toshihiko Yamasaki
Although Convolutional Neural Networks (CNNs) have high accuracy in image recognition, they are vulnerable to adversarial examples and out-of-distribution data, and the difference…
Prediction of Seismic Intensity Distributions Using Neural Networks
Koyu Mizutani, Haruki Mitarai, Kakeru Miyazaki +3
The ground motion prediction equation is commonly used to predict the seismic intensity distribution. However, it is not easy to apply this method to seismic distributions affected…
Superclass Adversarial Attack
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Adversarial attacks have only focused on changing the predictions of the classifier, but their danger greatly depends on how the class is mistaken. For example, when an automatic d…