- The paper introduces a novel abstention loss function that lets classifiers withhold judgment on ambiguous samples to mitigate label noise.
- It employs an extra abstain label during training and inference, enabling the model to isolate and handle systematic noise.
- Empirical results on CIFAR-10, CIFAR-100, and Fashion-MNIST show the Deep Abstaining Classifier significantly outperforms standard models in noisy settings.
Combating Label Noise in Deep Learning Using Abstention
The paper "Combating Label Noise in Deep Learning Using Abstention" presents a novel mechanism to address the systemic problem of label noise in deep neural networks (DNNs) through a strategy termed abstention. At its core, the paper introduces an abstention-based approach that allows a classifier to abstain from making predictions on samples where label noise may lead to uncertainty, thereby enhancing the classifier's performance on cleaner samples.
The central innovation of this work is an abstention loss function that integrates a strategic withdrawal from classification when encountering ambiguous or noisy data. This approach operates both during training and inference, diverging from traditional methods that often apply abstention only as post-processing. The abstention mechanism is particularly potent in scenarios featuring structured or systematic noise, as it enables the DNN to recognize and learn features linked to unreliable training labels. This is achieved through an explicit "abstain" label added to the traditional set of output classes, where the network learns to defer judgment rather than risk a misclassification based on inaccurate labels.
The paper provides thorough theoretical analysis and empirical evidence supporting the efficacy of the proposed method across various types of label noise, structured and unstructured alike. In structured noise cases, where patterns of errors correlate with inherent data features, the Deep Abstaining Classifier (DAC) can isolate these patterns through its loss adaptation dynamics, allowing for a nuanced representation learning that preempts potential misclassifications. For arbitrary label noise, the DAC serves as a robust data cleaner, adept at flagging samples with probable label corruption, therefore optimizing downstream models trained with the purified dataset.
Noteworthy experimental results are detailed across multiple benchmarks including CIFAR-10, CIFAR-100, and Fashion-MNIST. The DAC consistently outperforms existing models, specifically in scenarios of high label noise, achieving superior accuracy by significant margins — sometimes recovering close to the "oracle performance" obtained with perfect a priori noise knowledge.
The implications of this research extend both to theoretical advancements in loss function design for classification tasks and practical applications in data-intensive fields. It positions the DAC as an attractive option in large-scale machine learning deployments where the quality and reliability of labels cannot always be guaranteed. While the paper does not address adversarial settings, the theoretical underpinnings suggest that abstention could potentially strengthen defenses against adversarial perturbations — an avenue ripe for future exploration.
Moving forward, this abstention mechanism could be integrated with other model architectures and domains, suggesting broader applicability beyond image classification tasks. The simplicity of the DAC's design—requiring only modification of the loss function—presents a versatile tool that could be seamlessly incorporated into current DNN frameworks to bolster robustness against label noise. As the AI community continues to grapple with the complexities of real-world data environments, strategies like abstention may well become pivotal in reinforcing the robustness and reliability of deep learning models.