Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias

Core Concepts

The author explores the impact of simplicity bias on neural networks and proposes Spare as a method to identify and mitigate spurious correlations early in training.

Abstract

The content discusses how neural networks trained with gradient descent can learn spurious correlations due to simplicity bias. It introduces Spare, a method that outperforms existing techniques by up to 21.1% in worst-group accuracy while being faster. Theoretical analysis and empirical contributions are provided to support the effectiveness of Spare. Neural networks have an inductive bias towards learning simpler solutions, making them prone to spurious correlations during training. Spare is proposed as a lightweight method to identify and alleviate these biases early on, leading to improved worst-group accuracy without extensive hyperparameter tuning. Key points include the theoretical analysis showing how spurious features are learned early in training, the separability of majority and minority groups based on network output, and the effectiveness of importance sampling in mitigating spurious correlations. Spare's performance is compared against state-of-the-art methods on various benchmark datasets, showcasing its superiority in identifying and addressing spurious biases.

Stats

Empirically, Spare outperforms state-of-the-art methods by up to 21.1% in worst-group accuracy. Spare achieves up to 12x faster computational speed compared to existing methods.

Quotes

"No theoretical guideline for finding the time of group inference and group weights." "Spare clusters model’s output early in training based on importance sampling." "Spare operates without a group-labeled validation data."

Key Insights Distilled From

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias

by Yu Yang,Eric... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2305.18761.pdf

Deeper Inquiries

How can simplicity bias be further mitigated in neural networks

One way to further mitigate simplicity bias in neural networks is by incorporating regularization techniques. Regularization methods such as L1 and L2 regularization can help prevent overfitting and encourage the model to focus on important features rather than spurious correlations. Additionally, techniques like dropout and batch normalization can also be used to improve generalization performance and reduce the impact of simplicity bias. Another approach is to use more complex network architectures that are less prone to learning spurious correlations, such as attention mechanisms or capsule networks.

What are potential limitations or drawbacks of using importance sampling for identifying spurious correlations

While importance sampling can be effective in identifying and mitigating spurious correlations, there are potential limitations and drawbacks associated with this approach. One limitation is the need for accurate clustering of examples based on their output values, which may not always be straightforward or reliable, especially in high-dimensional spaces. Additionally, importance sampling relies on the assumption that the clusters accurately represent minority groups without any overlap or misclassification, which may not always hold true in practice. Moreover, determining appropriate cluster sizes and weights for importance sampling can be challenging and require careful tuning.

How might the findings from this study impact future research on bias mitigation in machine learning algorithms

The findings from this study have significant implications for future research on bias mitigation in machine learning algorithms. By demonstrating how simplicity bias contributes to learning spurious correlations early in training, researchers can develop more targeted strategies for addressing these biases effectively. The proposed method Spare provides a theoretical framework for identifying and mitigating spurious correlations early in training without relying on group-labeled validation data. This could inspire further research into developing lightweight yet effective techniques for discovering and eliminating biases in neural networks across various applications domains.