Robust Long-Tailed Learning under Label Noise

by   Tong Wei, et al.

Long-tailed learning has attracted much attention recently, with the goal of improving generalisation for tail classes. Most existing works use supervised learning without considering the prevailing noise in the training dataset. To move long-tailed learning towards more realistic scenarios, this work investigates the label noise problem under long-tailed label distribution. We first observe the negative impact of noisy labels on the performance of existing methods, revealing the intrinsic challenges of this problem. As the most commonly used approach to cope with noisy labels in previous literature, we then find that the small-loss trick fails under long-tailed label distribution. The reason is that deep neural networks cannot distinguish correctly-labeled and mislabeled examples on tail classes. To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise. Based on the above findings, we propose a robust framework, , that realizes noise detection for long-tailed learning, followed by soft pseudo-labeling via both label smoothing and diverse label guessing. Moreover, our framework can naturally leverage semi-supervised learning algorithms to further improve the generalisation. Extensive experiments on benchmark and real-world datasets demonstrate the superiority of our methods over existing baselines. In particular, our method outperforms DivideMix by 3% in test accuracy. Source code will be released soon.


Combating Noisy-Labeled and Imbalanced Data by Two Stage Bi-Dimensional Sample Selection

Robust learning on noisy-labeled data has been an important task in real...

Friends and Foes in Learning from Noisy Labels

Learning from examples with noisy labels has attracted increasing attent...

APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning

Practical natural language processing (NLP) tasks are commonly long-tail...

Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition

In real-world scenarios, collected and annotated data often exhibit the ...

Adversarial Robustness under Long-Tailed Distribution

Adversarial robustness has attracted extensive studies recently by revea...

Robust Long-Tailed Learning via Label-Aware Bounded CVaR

Data in the real-world classification problems are always imbalanced or ...

Long-Tailed Partial Label Learning via Dynamic Rebalancing

Real-world data usually couples the label ambiguity and heavy imbalance,...

Please sign up or login with your details

Forgot password? Click here to reset