Noise Injection

Noise injection is a technique used in various fields, including machine learning and data processing, to enhance the robustness of models or systems. It involves deliberately adding random variations or "noise" to data during training or testing. This helps prevent overfitting, where a model performs well on training data but poorly on new, unseen data. By exposing the model to these variations, it learns to generalize better and become more resilient to real-world unpredictabilities, leading to improved performance when encountering diverse situations or inputs.