Uncovering and Overcoming Implicit Bias in Data Pruning for Robust Deep Learning Models

Core Concepts

Existing data pruning algorithms can produce highly biased classifiers, sacrificing performance on difficult classes to retain strong average accuracy. A fairness-aware pruning approach with random subsampling according to class-wise error rates can significantly improve the worst-class accuracy while maintaining high average performance.

Abstract

The content discusses the issue of classification bias in deep learning models, which can be exacerbated by existing data pruning techniques. It presents a comprehensive evaluation of various pruning algorithms through the lens of fairness, revealing that current methods often fail to improve, and in some cases even worsen, the performance disparity across classes. The paper proposes a "fairness-aware" pruning approach called MetriQ, which selects class-wise pruning ratios based on the corresponding class-wise error rates computed on a hold-out validation set. When combined with random subsampling within classes, MetriQ is shown to consistently outperform other pruning algorithms in terms of both average and worst-class accuracy across standard computer vision benchmarks. The authors provide theoretical analysis in a toy Gaussian mixture model setting, which sheds light on the fundamental principles behind the success of MetriQ. The analysis suggests that random pruning with appropriate class ratios has the potential to improve the worst-class performance, in contrast to existing pruning methods that often sacrifice difficult classes to retain strong average accuracy.

Stats

The content does not provide any specific numerical data or statistics. It focuses on the conceptual and empirical evaluation of data pruning algorithms with respect to fairness.

Quotes

None.

Key Insights Distilled From

Robust Data Pruning

by Artem Vysogo... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05579.pdf

Deeper Inquiries

How can the insights from the Gaussian mixture model analysis be extended to more complex, high-dimensional deep learning settings

The insights gained from the Gaussian mixture model analysis can be extended to more complex, high-dimensional deep learning settings by considering the underlying principles of optimal data pruning for robust model performance. In the Gaussian mixture model, the optimal class-wise densities were determined based on the class priors and variances to achieve the best worst-class error. This concept can be translated to deep learning settings by adapting the idea of selecting data samples based on their difficulty or uncertainty metrics. In high-dimensional deep learning scenarios, where the feature space is complex and non-linear, the notion of selecting samples that contribute most to the learning process can be crucial. By incorporating metrics that capture the importance or relevance of individual samples, similar to the variance-based pruning quotas in the Gaussian mixture model, one can effectively prune the training data to enhance model robustness and generalization. This approach aligns with the concept of data efficiency in deep learning, where the focus is on selecting informative samples while discarding redundant or noisy ones. Furthermore, in high-dimensional settings, the challenge of overfitting and model complexity is prevalent. By applying the principles of optimal data pruning derived from the Gaussian mixture model analysis, one can potentially improve the generalization capabilities of deep learning models by focusing on the most relevant training samples while reducing the risk of overfitting to noisy or irrelevant data points.

Can the MetriQ approach be combined with other fairness-driven optimization techniques, such as cost-sensitive learning, to further improve the robustness of the trained models

The MetriQ approach, which involves random subsampling based on difficulty-based class-wise ratios, can indeed be combined with other fairness-driven optimization techniques, such as cost-sensitive learning, to further enhance the robustness of trained models. Cost-sensitive learning methods aim to address the performance disparities across different classes by assigning weights to samples based on their difficulty or importance. By integrating MetriQ with cost-sensitive learning, one can create a more comprehensive and effective approach to improving model fairness and robustness. In this combined approach, MetriQ can be used to determine the class-wise pruning ratios, while cost-sensitive learning techniques can be employed to assign appropriate weights to the samples within each class. By leveraging the strengths of both methods, the model can benefit from a more nuanced and tailored approach to data pruning, leading to improved worst-class accuracy and overall fairness. By integrating MetriQ with cost-sensitive learning, the model can achieve a better balance between average performance and classification bias, ultimately resulting in more robust and fair deep learning models.

What are the potential implications of fairness-aware data pruning on other aspects of model performance, such as out-of-distribution generalization or adversarial robustness

The potential implications of fairness-aware data pruning on other aspects of model performance, such as out-of-distribution generalization or adversarial robustness, are significant. Fairness-aware data pruning focuses on selecting training samples that contribute to reducing classification bias and improving worst-class accuracy. These implications can extend to other areas of model performance as follows: Out-of-Distribution Generalization: Fairness-aware data pruning, by selecting informative and relevant samples, can potentially improve the model's ability to generalize to out-of-distribution data. By focusing on the most important samples during training, the model may learn more robust and generalizable features that can better handle unseen data points. Adversarial Robustness: Fairness-aware data pruning can also impact the model's robustness against adversarial attacks. By reducing bias and improving worst-class accuracy, the model may become more resilient to adversarial perturbations that target vulnerable areas of the data distribution. This enhanced robustness can help mitigate the impact of adversarial examples on model performance. Overall, fairness-aware data pruning has the potential to not only improve fairness and robustness in model predictions but also enhance other aspects of model performance, such as out-of-distribution generalization and adversarial robustness.