Confident Learning: Estimating Uncertainty in Dataset Labels (1911.00068v6)

Abstract: Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly finds label errors, and show CL performance exceeding seven recent competitive approaches for learning with noisy labels on the CIFAR dataset. Uniquely, the CL framework is not coupled to a specific data modality or model (e.g., we use CL to find several label errors in the presumed error-free MNIST dataset and improve sentiment classification on text data in Amazon Reviews). We also employ CL on ImageNet to quantify ontological class overlap (e.g., estimating 645 "missile" images are mislabeled as their parent class "projectile"), and moderately increase model accuracy (e.g., for ResNet) by cleaning data prior to training. These results are replicable using the open-source cleanlab release.

• The paper introduces Confident Learning to directly estimate label noise and identify mislabeled data, improving overall dataset quality.
• It details a framework that leverages probabilistic counting and example ranking, achieving over 30% improvement on noisy CIFAR-10 experiments.
• The methodology provides theoretical guarantees and is validated across diverse datasets, including ImageNet and MNIST, for robust error estimation.

Confident Learning: Estimating Uncertainty in Dataset Labels

The paper "Confident Learning: Estimating Uncertainty in Dataset Labels" presents a data-centric approach to address label noise in datasets, a significant challenge that impacts the reliability of machine learning models. As datasets grow larger and more complex, label errors become increasingly prevalent, necessitating robust mechanisms to manage these inaccuracies.

The core contribution of this work is the introduction of Confident Learning (CL), which directly estimates label noise by identifying label errors and improving the training process. Unlike previous model-centric strategies that focus on modifying models or loss functions, CL shifts the focus to assessing the quality of labels themselves. This is achieved by combining existing principles of pruning noisy data, probabilistic counting, and example ranking.

The CL Framework

The paper defines CL as a framework capable of identifying and quantifying label errors across datasets, functioning independently of any particular model or data type. The framework operates under the assumption of class-conditional noise, facilitating the estimation of the joint distribution between noisy and true labels. Notably, CL is demonstrably effective on multiple datasets, including CIFAR, MNIST, and ImageNet, showcasing its versatility.

Strong Numerical Results

The authors provide compelling evidence of CL's effectiveness through experimental results. For instance, CL outperformed methods like MentorNet on CIFAR-10, achieving an impressive 82% accuracy with 40% synthetic non-uniform label noise, marking a 34% improvement. The ability to surpass other approaches by at least 30% under these conditions underscores the robustness of CL in noisy environments.

Moreover, a significant finding is the identification of label errors in highly regarded datasets like ImageNet and MNIST. In ImageNet, CL identified 645 images mislabeled as "missiles" instead of "projectiles," demonstrating its potential for practical dataset improvements.

Theoretical Contributions

Beyond empirical success, the paper provides theoretical foundations for CL, proving its capability to accurately estimate label errors under certain assumptions. Specifically, the framework is theoretically validated to yield exact estimates of class-conditional errors and maintain robustness even when these assumptions are slightly violated.

Implications and Future Directions

The advent of CL illustrates a critical shift towards incorporating label quality assessment in the AI pipeline. By improving the accuracy of dataset labels, CL enhances model reliability without necessitating substantial computational overhead. The open-source nature of the cleanlab package facilitates the replication and adoption of these methodologies across various applications.

Looking forward, the implications of this research are profound. It paves the way for further exploration into uncertainty estimation techniques, particularly as AI systems continue to rely on increasingly large datasets. Future research might extend CL to handle more complex noise patterns or integrate it with curriculum learning to dynamically adjust training strategies based on label quality.

In conclusion, Confident Learning provides a robust tool for managing label noise, crucial for advancing the performance and trustworthiness of machine learning models. By prioritizing label quality, this work addresses a foundational challenge in data-driven AI developments.