Probabilistic End-To-End Noise Correction for Learning With Noisy Labels
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels
Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end …