Using Pre-Training Can Improve Model Robustness and Uncertainty (1901.09960v5)

Abstract: He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training. We show that although pre-training may not improve performance on traditional classification metrics, it improves model robustness and uncertainty estimates. Through extensive experiments on adversarial examples, label corruption, class imbalance, out-of-distribution detection, and confidence calibration, we demonstrate large gains from pre-training and complementary effects with task-specific methods. We introduce adversarial pre-training and show approximately a 10% absolute improvement over the previous state-of-the-art in adversarial robustness. In some cases, using pre-training without task-specific methods also surpasses the state-of-the-art, highlighting the need for pre-training when evaluating future methods on robustness and uncertainty tasks.

• The paper demonstrates that adversarial pre-training yields a 10% increase in adversarial accuracy over traditional methods, highlighting its role in robustness.
• The paper shows that pre-training significantly mitigates label corruption and class imbalance, reducing error rates and dependency on clean data.
• The paper finds that pre-training enhances uncertainty estimation and calibration, achieving higher AUROC/AUPR scores and lower RMS and MAD errors.

Using Pre-Training to Enhance Model Robustness and Uncertainty

The paper "Using Pre-Training Can Improve Model Robustness and Uncertainty" by Hendrycks, Lee, and Mazeika addresses the efficacy of pre-training in deep learning models, specifically those used in convolutional neural networks. While earlier studies have questioned the need for pre-training due to no traditional accuracy improvements, this paper demonstrates its substantial benefits in robustness and uncertainty estimation tasks.

Key Contributions

1. Adversarial Robustness: The authors propose adversarial pre-training, significantly improving model performance against adversarial attacks. Their results demonstrate a 10% absolute improvement in adversarial accuracy over the prior art, establishing the importance of considering pre-training when developing defenses against adversarial noise. Pre-trained models exhibit resilience, often surpassing task-specific methods without further tuning.
2. Label Corruption and Data Imbalance: The paper explores the impact of label noise and class imbalance. Pre-training markedly enhances robustness against label corruption, effectively outperforming previous methods. Combining pre-training with existing methods reduces reliance on trusted data, a critical factor in large-scale, noisy datasets. For class imbalance, pre-training demonstrates lower error rates across all tested ratios, primarily benefiting minority classes.
3. Uncertainty Estimation and Calibration: The authors emphasize the improvement in uncertainty estimation through out-of-distribution detection and better calibration. Pre-training models achieve higher AUROC and AUPR scores across multiple datasets, indicating more effective anomaly detection. Regarding calibration, pre-training reduces RMS and MAD calibration errors, indicating substantial improvements in confidence estimation without additional data-driven interventions.

Implications and Future Developments

The findings substantially contribute to deep learning, especially in safety-critical applications where robustness and uncertainty are paramount. They suggest a paradigm shift in evaluating future models, urging researchers to incorporate pre-training in tasks beyond classification accuracy.

Potential future explorations may involve:

• Specializing pre-training techniques for specific robustness tasks.
• Investigating the impact of varied pre-training datasets on downstream robustness and uncertainty performance.
• Extending pre-training benefits to more complex architectures and domains, including natural language processing and reinforcement learning.

Conclusion

This paper advocates for the broader application of pre-training in enhancing robustness and uncertainty metrics in deep learning models. It provides evidence that pre-training, although not improving conventional accuracy measures, significantly enriches model performance in adversarial scenarios, label corruption, class imbalance, and uncertainty tasks. Adopting such strategies could lead to more resilient and reliable AI systems, widening the applicability of deep learning in challenging environments.