Probabilistic End-to-end Noise Correction for Learning with Noisy Labels (1903.07788v1)

Abstract: Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions. PENCIL is independent of the backbone network structure and does not need an auxiliary clean dataset or prior information about noise, thus it is more general and robust than existing methods and is easy to apply. PENCIL outperforms previous state-of-the-art methods by large margins on both synthetic and real-world datasets with different noise types and noise rates. Experiments show that PENCIL is robust on clean datasets, too.

• The paper introduces an end-to-end noise correction method that redefines labels as probabilistic distributions to mitigate overfitting in noisy datasets.
• It employs a variant of KL-divergence loss combined with entropy and compatibility losses to iteratively refine label accuracy throughout training.
• Experimental results on CIFAR-10, CIFAR-100, and Clothing1M validate that PENCIL significantly improves model performance under diverse noise conditions.

An Overview of PENCIL: Probabilistic End-to-end Noise Correction for Learning with Noisy Labels

The paper "Probabilistic End-to-end Noise Correction for Learning with Noisy Labels" by Kun Yi and Jianxin Wu introduces a robust framework called PENCIL designed to address the challenge of learning with noisy labels in supervised deep learning environments. Noisy labels are a significant issue, particularly in large-scale datasets where manual verification is impractical, leading to a degradation in model performance due to overfitting. This paper provides a comprehensive approach to mitigate such challenges by leveraging probabilistic modeling within deep learning frameworks.

Framework Overview

PENCIL is an end-to-end noise correction framework that treats labels as probabilistic distributions rather than fixed categorical values. This enables a more flexible and adaptive representation, crucial for handling noise in labels effectively. Unlike some existing methods, PENCIL does not depend on the backbone network architecture nor requires an auxiliary clean dataset or prior information about noise, making it applicable across a range of scenarios.

The core of the PENCIL approach revolves around three types of labels—probabilistic label distributions, noisy labels, and initialized labels—as parameters that are iteratively updated using back-propagation during the training process. This probability-driven label representation allows the network not only to learn model parameters but also to refine label noise progressively. By iteratively updating the label distributions, the framework provides a mechanism to correct erroneous labels effectively.

Loss Functions

Central to the PENCIL framework is the adoption of a variant of the KL-divergence loss function, which contrasts it with the traditional KL-loss. This adjusted loss function is vital for the noise correction process, offering sensible gradient flows adapted to updating label distributions accurately. The methodology integrates three components: the classification loss, an entropy loss for regularization to prevent premature convergence, and a compatibility loss that constrains label distributions from diverging unnecessarily from their initial noisy states.

Experimental Validation

The paper substantiates its claims with a battery of experiments across datasets with both synthetic and real-world noise, including CIFAR-10, CIFAR-100, CUB-200, and Clothing1M. In controlled noise settings, results demonstrate that PENCIL significantly outperforms conventional methods and other robust alternatives in various noise conditions, achieving notable accuracy improvements. Especially notable is its performance on real-world datasets like Clothing1M, where it achieves state-of-the-art results with substantial accuracy gains.

Implications and Future Directions

The PENCIL framework has profound implications from both theoretical and practical standpoints. Theoretically, it contributes a novel perspective on label noise management through probabilistic frameworks within deep neural networks. Practically, it promotes more reliable deployment of deep learning models in scenarios with unavoidable label noise. The approach is versatile and can enhance the robustness of models in real-world applications where label quality is variable.

Given its applicability across network types and noise conditions, the future direction for PENCIL could involve extensions to more complex data modalities beyond vision, exploring its integration with unsupervised or semi-supervised learning settings, or further optimizing the probabilistic model updating process for speed and efficiency in larger datasets.

In conclusion, PENCIL represents a significant advance in the domain of noise-resilient learning frameworks. Its end-to-end design and innovative use of probabilistic modeling establish it as a versatile tool for enhancing the robustness and accuracy of neural networks faced with noisy data environments.