Based Regularization

Based regularization is a technique used to improve the performance and explainability of machine learning models by strategically masking or modifying input data during training. Current research focuses on integrating this approach into various model architectures, including diffusion models and convolutional neural networks, often employing techniques like masked image modeling and dual-teacher learning to enhance both model accuracy and the generation of meaningful explanations for predictions. This approach is proving valuable across diverse applications, from medical image analysis (detecting anomalies in brain MRIs) to gait recognition and improving the trustworthiness of AI systems by providing insights into their decision-making processes.