👉 Confusion engineering is a technique used in machine learning to intentionally create or amplify confusion between different models or predictions to improve their robustness and generalization. By deliberately introducing noise, mislabeling data, or manipulating model outputs, engineers can force models to learn more generalized features that are less dependent on specific training patterns or biases. This approach helps in reducing overfitting and enhances the models' ability to perform well on unseen data by making them less sensitive to minor variations or adversarial attacks. Essentially, it's about training models to be more resilient by making them work harder to distinguish between similar but distinct predictions.
