Channel-Wise Contrastive Learning for Learning with Noisy Labels
Hui Kang Sheng Liu Huaxi Huang Tongliang Liu
In real-world datasets, noisy labels are pervasive. The challenge of learning
with noisy labels (LNL) is to train a classifier that discerns the actual
classes from given instances. For this, the model must identify features
indicative of the authentic labels. While research indicates that genuine label
information is embedded in the learned features of even inaccurately labeled
data, it's often intertwined with noise, complicating its direct application.
Addressing this, we introduce channel-wise contrastive learning (CWCL). This
method distinguishes authentic label information from noise by undertaking
contrastive learning across diverse channels. Unlike conventional instance-wise
contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient
features aligned with the authentic labels. Our strategy is twofold: firstly,
using CWCL to extract pertinent features to identify cleanly labeled samples,
and secondly, progressively fine-tuning using these samples. Evaluations on
several benchmark datasets validate our method's superiority over existing
approaches.