Why to Add Noise to Images for Machine Learning

Computer vision models aim to generalize to real-world scenarios, but often struggle with overfitting to training data rather than adapting to unforeseen situations. Introducing noise into training datasets can help models learn patterns rather than memorizing, thereby improving their ability to handle unexpected inputs. Noise, such as salt-and-pepper noise, involves altering pixel data in images, which can be applied as a preprocessing or augmentation technique to improve model robustness against adversarial attacks. Research from Arizona State University highlights that noise and blurring significantly impact model accuracy, necessitating their strategic use during training to prevent overfitting without affecting validation or testing sets. Tools like scikit-image and Roboflow offer implementations to vary noise levels, enabling users to explore its effects and optimize model performance.