135 citations · 298 across the 23 of their papers we have counts for
5 papers · 1 filter
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation
Dongyoon Yang, Insung Kong, Yongdai Kim
Adversarial robustness is a research area that has recently received a lot of attention in the quest for trustworthy artificial intelligence. However, recent works on adversarial r…
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Dongyoon Yang, Kunwoong Kim, Yongdai Kim
Semi-supervised learning (SSL) algorithm is a setup built upon a realistic assumption that access to a large amount of labeled data is tough. In this study, we present a generalize…
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
Dongha Kim, Yongchan Choi, Kunwoong Kim +1
In many classification problems, collecting massive clean-annotated data is not easy, and thus a lot of researches have been done to handle data with noisy labels. Most recent stat…
Kernel-convoluted Deep Neural Networks with Data Augmentation
Minjin Kim, Young-geun Kim, Dongha Kim +2
The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robust…
Primal path algorithm for compositional data analysis
Jong-June Jeon, Yongdai Kim, Sungho Won +1
Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these cha…