Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·14d ago·5 min read
Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization
TL;DR
Gradient-Energy Adaptive Radius SAM (GEAR-SAM) enhances Sharpness-Aware Minimization (SAM) by dynamically redistributing perturbation budgets based on block sensitivity, leading to improved model generalization and robustness across various tasks.
✦ Why It Matters
Implement GEAR-SAM in your training pipeline to enhance model robustness and generalization today.
Key Takeaways
- Adopt GEAR-SAM for better model generalization in your projects.
- Utilize exponential moving averages for sensitivity tracking in training.
- Experiment with perturbation budget redistribution during training.
- Evaluate GEAR-SAM's performance across different architectures and tasks.
How It Works
GEAR-SAM improves upon traditional SAM by using an exponential moving average (EMA) of squared block gradients to assess sensitivity. This allows for a more informed allocation of the perturbation budget, ensuring that it reflects the evolving sensitivity of different parameter blocks throughout training.
The method employs a closed-form constrained optimization to distribute the fixed budget effectively, enhancing the model's ability to generalize.