Dynamic Model Training with Adversarial Data

This post introduces a method for dynamically training models using adversarial data. It details how to generate adversarial examples and incorporate them into the training loop to improve model robustness and performance. The approach aims to make models more resilient to subtle perturbations in input data, a crucial aspect for real-world applications where data can be noisy or manipulated.