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

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.

Weight Decay Scheduling and Knowledge Distillation for Active Learning

no code implementations • ECCV 2020 • Juseung Yun, Byungjoo Kim, Junmo Kim

Active learning is a technique that can reduce the labeling required.

Active Learning Knowledge Distillation +1