  Learning with noisy labels (LNL) aims at designing strategies to improve
model performance and generalization by mitigating the effects of model
overfitting to noisy labels. The key success of LNL lies in identifying as many
clean samples as possible from massive noisy data, while rectifying the wrongly
assigned noisy labels. Recent advances employ the predicted label distributions
of individual samples to perform noise verification and noisy label correction,
easily giving rise to confirmation bias. To mitigate this issue, we propose
Neighborhood Collective Estimation, in which the predictive reliability of a
candidate sample is re-estimated by contrasting it against its feature-space
nearest neighbors. Specifically, our method is divided into two steps: 1)
Neighborhood Collective Noise Verification to separate all training samples
into a clean or noisy subset, 2) Neighborhood Collective Label Correction to
relabel noisy samples, and then auxiliary techniques are used to assist further
model optimization. Extensive experiments on four commonly used benchmark
datasets, i.e., CIFAR-10, CIFAR-100, Clothing-1M and Webvision-1.0, demonstrate
that our proposed method considerably outperforms state-of-the-art methods.

Computer Vision and Pattern Recognition