Residual Classifier

Residual classifiers leverage the difference between a predicted output and the actual output (the residual) to improve model performance and address various challenges in machine learning. Current research focuses on applying this concept across diverse domains, including control systems, image processing, and anomaly detection, often employing architectures like convolutional neural networks, Gaussian processes, and random forests to learn and utilize these residuals effectively. This approach enhances model accuracy, efficiency, and robustness in scenarios with uncertainty, imbalanced data, or contaminated datasets, leading to improvements in applications ranging from autonomous driving to medical imaging.