274 citations · 423 across the 23 of their papers we have counts for
4 papers · 1 filter
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Jongheon Jeong, Sejun Park, Minkyu Kim +3
Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against -adversarial perturbations. Under the…
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Jongjin Park, Sukmin Yun, Jongheon Jeong +1
Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations. In this paper, we focus on a practical scenario that one a…
Training GANs with Stronger Augmentations via Contrastive Discriminator
Jongheon Jeong, Jinwoo Shin
Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is…
Consistency Regularization for Adversarial Robustness
Jihoon Tack, Sihyun Yu, Jongheon Jeong +3
Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfittin…