如何使用對抗性數據動態訓練你的模型 (以 MNIST 為例)

This technical blog post from Hugging Face takes an in-depth look at how to build a closed-loop system for "Dynamic Adversarial Training." In a traditional machine learning workflow, models are typically trained and evaluated on a static dataset. However, this approach has clear limitations: once a model is deployed to a real production environment, its performance often degrades sharply when confronted with edge cases, data distribution shifts, or adversarial inputs that were never seen during training.