A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Learning from Incorrectly Labeled Data

1. 1Identify adversarial examples in the dataset
2. 2Use these examples as additional training data to improve model generalization
3. 3Analyze the results to understand how the model is learning from errors
4. 4Apply this knowledge to improve model robustness and performance

ML researchers and engineers can benefit from understanding how adversarial examples can be leveraged to improve model performance, and data scientists can apply this knowledge to improve model robustness

💡 Adversarial examples are not just errors, but can be used as features to improve model performance

🚀 Adversarial examples can improve model generalization