Search Results for author: Youngdong Kim

Found 3 papers, 1 papers with code

BiasAdv: Bias-Adversarial Augmentation for Model Debiasing

no code implementations CVPR 2023 Jongin Lim, Youngdong Kim, Byungjai Kim, Chanho Ahn, Jinwoo Shin, Eunho Yang, Seungju Han

Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model.

Adversarial Attack Data Augmentation

Joint Negative and Positive Learning for Noisy Labels

no code implementations CVPR 2021 Youngdong Kim, Juseung Yun, Hyounguk Shon, Junmo Kim

Based on the fact that directly providing the label to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has proven to be highly effective in preventing overfitting to noisy data as it reduces the risk of providing faulty target.

NLNL: Negative Learning for Noisy Labels

1 code implementation ICCV 2019 Youngdong Kim, Junho Yim, Juseung Yun, Junmo Kim

The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images.

General Classification Image Classification

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