13 citations · 20 across the 9 of their papers we have counts for
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cs.LG2026
What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Kunwoong Kim, Dongha Kim
Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised set…
cs.LG2021
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…
cs.LG2020
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…