Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels (1905.05040v1)

Abstract: Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels. In this paper, we find that the test accuracy can be quantitatively characterized in terms of the noise ratio in datasets. In particular, the test accuracy is a quadratic function of the noise ratio in the case of symmetric noise, which explains the experimental findings previously published. Based on our analysis, we apply cross-validation to randomly split noisy datasets, which identifies most samples that have correct labels. Then we adopt the Co-teaching strategy which takes full advantage of the identified samples to train DNNs robustly against noisy labels. Compared with extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs under both synthetic and real-world training noise.

• The paper proposes the Iterative Noisy Cross-Validation (INCV) method to mitigate the effects of both symmetric and asymmetric label noise.
• It establishes a quadratic relationship between test accuracy and noise ratio, providing a theoretical foundation that aligns with prior empirical findings.
• Extensive experiments on synthetic and real-world datasets demonstrate that the proposed approach outperforms existing methods in robust DNN training.

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

The paper entitled "Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels," authored by Pengfei Chen et al., provides a rigorous analysis of how noisy labels affect the generalization performance of deep neural networks (DNNs) and proposes robust training strategies to handle such noise effectively. Given the ubiquity of noisy labels in real-world datasets, due to cost constraints on accurate labeling, this work is both relevant and timely for advancing robust machine learning applications.

Summary and Findings

The research investigates the impact of noisy labels on DNN training and provides a mathematical basis for understanding test accuracy behavior as a function of noise ratio. It reveals that test accuracy is a quadratic function of the noise ratio when symmetrically distributed noise affects the data. This relationship corroborates empirical results from prior studies, offering a fresh theoretical grounding.

To address the challenges posed by noisy labels, the authors propose the Iterative Noisy Cross-Validation (INCV) method, which enhances the Co-teaching strategy. INCV utilizes cross-validation to split data into subsets, thereby filtering out a clean subset of samples for training. This approach reduces the effect of mislabeled data during training, improving robustness against noise.

The paper also addresses asymmetric noise where corruptions are not uniformly distributed across classes. It demonstrates the INCV’s ability to handle such scenarios by integrating a component to estimate the noise ratio directly from the data.

Experimental Validation

Extensive experiments using both synthetic datasets such as CIFAR-10, which are manually corrupted by known noise distributions, and real-world datasets like WebVision, containing naturally occurring noisy labels, validate the claims. The proposed algorithm consistently outperforms state-of-the-art methods, such as F-correction, Decoupling, and standard Co-teaching, which demonstrates its effectiveness in practical scenarios.

Implications and Future Directions

This work has significant implications for improving the reliability and accuracy of machine learning models in environments with suboptimal data labeling. The ability to automatically detect and robustify against noisy data extends the applicability of DNNs to settings previously deemed too risky or infeasible due to label noise.

Theoretically, the paper paves the way for further exploration into the boundaries of DNN generalization in noisy environments. The results highlight the need for continued research on noise-resistant training methodologies. Future developments could explore dynamic noise estimation techniques, scaling the approach to larger datasets, and incorporating these findings into unsupervised or semi-supervised learning contexts.

In conclusion, this paper provides a comprehensive framework to address the pervasive issue of label noise in deep learning, offering both theoretical insights and practical solutions that are instrumental for advancing robust AI systems capable of learning from less-than-ideal data scenarios.