Rethinking the Value of Labels for Improving Class-Imbalanced Learning (2006.07529v2)

Abstract: Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the value of labels in the context of imbalanced learning: on the one hand, supervision from labels typically leads to better results than its unsupervised counterparts; on the other hand, heavily imbalanced data naturally incurs "label bias" in the classifier, where the decision boundary can be drastically altered by the majority classes. In this work, we systematically investigate these two facets of labels. We demonstrate, theoretically and empirically, that class-imbalanced learning can significantly benefit in both semi-supervised and self-supervised manners. Specifically, we confirm that (1) positively, imbalanced labels are valuable: given more unlabeled data, the original labels can be leveraged with the extra data to reduce label bias in a semi-supervised manner, which greatly improves the final classifier; (2) negatively however, we argue that imbalanced labels are not useful always: classifiers that are first pre-trained in a self-supervised manner consistently outperform their corresponding baselines. Extensive experiments on large-scale imbalanced datasets verify our theoretically grounded strategies, showing superior performance over previous state-of-the-arts. Our intriguing findings highlight the need to rethink the usage of imbalanced labels in realistic long-tailed tasks. Code is available at https://github.com/YyzHarry/imbalanced-semi-self.

• The paper introduces semi-supervised learning techniques that use unlabeled data to reduce label bias in class-imbalanced datasets.
• The paper demonstrates that self-supervised pre-training yields more robust feature representations, leading to significantly lower error rates.
• The paper provides theoretical insights using Gaussian models, offering practical frameworks to integrate unlabeled data into learning systems.

Rethinking the Value of Labels for Improving Class-Imbalanced Learning

The paper "Rethinking the Value of Labels for Improving Class-Imbalanced Learning" by Yuzhe Yang and Zhi Xu addresses a critical challenge in modern machine learning: the tendency of real-world datasets to display long-tailed distributions, leading to significant class imbalance. This imbalance presents substantial obstacles to the development of effective deep learning models, particularly in applications where accuracy is paramount, such as autonomous driving and healthcare diagnostics.

Dilemma of Label Value in Imbalanced Learning

The authors identify a central dilemma in the role of labels within imbalanced learning. Supervision through labels generally enhances classifier accuracy compared to unsupervised approaches, evidencing their positive value. However, such labels can also introduce "label bias," where model decision boundaries become disproportionately influenced by majority classes. This dual nature of label interaction forms the core inquiry: How can the value of labels be maximally exploited to enhance class-imbalanced learning?

Semi-Supervised and Self-Supervised Approaches

The paper propose two novel strategies: leveraging labels in semi-supervised manners and exploring self-supervised pre-training.

Semi-Supervised Learning: By introducing additional unlabeled data into class-imbalanced datasets, the authors demonstrate a reduction in label bias through a pseudo-labeling strategy. This approach consistently achieves superior performance across various settings by balancing the dataset more effectively. Their experiments on CIFAR-10-LT and SVHN-LT show impressive reductions in error rates when extra unlabeled data complements the original dataset.

Self-Supervised Pre-Training: Contrary to the idea that labels are constantly advantageous, the authors argue for initial self-supervision, discarding label information temporarily to form robust feature representations. Theoretical evidence suggests that this can lead to classifiers with exponentially smaller error probabilities, despite initial data imbalance. Empirically, they demonstrate that models pre-trained in a self-supervised manner outperform conventional baselines across several benchmarks, including ImageNet-LT and iNaturalist.

Theoretical Contributions

The paper's strength lies in the combination of theoretical insights and empirical validation. The authors utilize Gaussian models to establish conditions under which unlabeled data benefits imbalanced learning. They offer comprehensive analyses of data relevance and class imbalance, ensuring that the semi- and self-supervised methods remain effective yet practical.

Implications and Future Directions

The work has several implications:

• Practical Implementation: By providing frameworks that are both theoretically grounded and empirically validated, the paper offers practical tools for addressing class imbalance in diverse applications.
• AI Development: It opens avenues for the integration of unlabeled datasets into existing supervised frameworks, supporting the development of more adaptable AI systems.
• Theoretical Exploration: The findings encourage deeper exploration into self-supervision's role in mitigating label bias, which could redefine traditional approaches to supervised learning.

Conclusion

This research contributes significantly to the field of imbalanced learning. By addressing the inherent challenges of label bias through innovative semi-supervised and self-supervised solutions, the authors provide a compelling case for revisiting how labels are utilized in AI systems. Future developments in AI may build upon these findings, potentially affecting fields relying heavily on imbalanced datasets.