Masking: A New Perspective of Noisy Supervision

NeurIPS 2018 Bo HanJiangchao YaoGang NiuMingyuan ZhouIvor TsangYa ZhangMasashi Sugiyama

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix... (read more)

PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification Clothing1M MASKING Accuracy 71.1% # 12

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet