How to train your model dynamically using adversarial data

1. 1Collect adversarial data by having humans create examples to fool state-of-the-art models
2. 2Use the collected data to further train the model
3. 3Repeat the process over multiple rounds to achieve a more robust model
4. 4Configure and interact with the model to optimize performance

Machine learning engineers and data scientists can benefit from this approach to improve model performance and robustness

💡 Dynamic adversarial data collection can help mitigate issues with static benchmarks and improve model trustworthiness

🚀 Improve model robustness with dynamic adversarial data collection!