research
∙
07/21/2022
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness
Defending deep neural networks against adversarial examples is a key cha...
share
research
∙
03/02/2021
Smoothness Analysis of Loss Functions of Adversarial Training
Deep neural networks are vulnerable to adversarial attacks. Recent studi...
research
∙
02/05/2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
research
∙
10/06/2020
Constraining Logits by Bounded Function for Adversarial Robustness
We propose a method for improving adversarial robustness by addition of ...
research
∙
03/26/2019
Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
research
∙
09/14/2018