Self-Adaptive Training: beyond Empirical Risk Minimization (2002.10319v2)

Abstract: We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data. This problem is crucial towards robustly learning from data that are corrupted by, e.g., label noises and out-of-distribution samples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noises and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly improves generalization over ERM under various levels of noises, and mitigates the overfitting issue in both natural and adversarial training. We evaluate the error-capacity curve of self-adaptive training: the test error is monotonously decreasing w.r.t. model capacity. This is in sharp contrast to the recently-discovered double-descent phenomenon in ERM which might be a result of overfitting of noises. Experiments on CIFAR and ImageNet datasets verify the effectiveness of our approach in two applications: classification with label noise and selective classification. We release our code at https://github.com/LayneH/self-adaptive-training.

• The paper shows that using model predictions to adapt training targets improves noise robustness and generalization.
• It employs an exponential-moving average and re-weighting mechanism to dynamically adjust training weights without extra cost.
• The approach avoids the double-descent phenomenon, achieving up to 9.3% accuracy gains on CIFAR datasets and improved adversarial robustness.

Overview of Self-Adaptive Training: Beyond Empirical Risk Minimization

The paper "Self-Adaptive Training: Beyond Empirical Risk Minimization" introduces a novel training methodology designed to enhance the generalization capabilities of deep learning models, particularly in contexts where training data may be corrupted. The self-adaptive training (SAT) algorithm dynamically calibrates the training process using model predictions, offering a robust alternative to empirical risk minimization (ERM), which is susceptible to overfitting in the presence of noise. The approach improves model performance without incurring additional computational costs.

Key Contributions and Findings

1. Analysis of ERM's Limitations: The authors critically examine the deficiencies of ERM, particularly its predilection for overfitting noisy data. Through experiments on the CIFAR10 dataset, the paper illustrates that ERM quickly fits corrupted labels, frequently leading to sub-par generalization. The paper confirms that model predictions hold valuable information which, if leveraged appropriately, can enhance training outcomes.
2. Self-Adaptive Training Approach: Utilizing model predictions, SAT incorporates:
   • An exponential-moving-average mechanism that updates class labels dynamically, which helps in stabilizing training targets.
   • A re-weighting scheme that adjusts weights on training samples based on prediction confidence—placing less emphasis on erroneous data and more on correct predictions.
3. Single-Descent Error-Capacity Curve: SAT avoids the recently highlighted double-descent phenomenon observed in traditional ERM. The single-descent behavior indicates SAT’s robustness against noise, suggesting that previous observations of double-descent might be tied to overfitting problems inherent in ERM.
4. Empirical Results: The experiments on both CIFAR and ImageNet datasets demonstrate significant improvements. Notably, SAT improves classification accuracy by up to 9.3% on CIFAR datasets under label noise and enhances adversarial robustness by approximately 3%.
5. Applications: SAT is applied to:
   • Classification with Label Noise: It demonstrates superior performance over state-of-the-art methods, achieving up to 9.3% improvement on noisy datasets.
   • Selective Classification: Here, the goal is to trade-off accuracy for prediction coverage, with SAT showing up to 50% relative improvements over existing methods.

Implications and Future Directions

The proposed SAT presents meaningful implications for both practical and theoretical dimensions of AI research:

• Practical Implications: SAT’s efficiency in processing noisy data suggests its utility in real-world applications where datasets are inherently imperfect—ranging from autonomous driving to medical image analysis, where data quality is variable.
• Theoretical Insights: The single-descent curve challenges established paradigms around bias-variance trade-offs in deep learning, suggesting avenues for further theoretical exploration into learning complexities and regularization techniques.
• Generalizability and Robustness: By integrating model predictions into the learning process, SAT provides a framework that might extend to other machine learning tasks beyond supervised learning, including reinforcement learning and unsupervised learning.

This paper paves the way for further investigations into adaptive training methodologies, with situational applications in environments where data integrity cannot be assured. Furthermore, given the potential implications for model interpretability and robustness, self-adaptive training holds significant promise for evolving AI systems that need to operate reliably in real-world scenarios. Future research might focus on integrating SAT with alternative loss functions and exploring its efficacy across diverse network architectures and learning paradigms.