Search Results for author: Youngdong Kim

Found 2 papers, 1 papers with code

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

Cannot find the paper you are looking for? You can Submit a new open access paper.