Weight Mask

Weight masking is a technique used in machine learning to selectively modify or suppress the influence of specific model parameters, achieving various objectives such as improving fairness, enhancing image reconstruction, and boosting model robustness. Current research focuses on developing novel weight masking algorithms and integrating them into diverse architectures, including transformers and convolutional neural networks, for applications ranging from medical image analysis and natural language processing to computer vision and MRI reconstruction. These advancements aim to address challenges like model bias, improve efficiency, and enhance the generalization capabilities of deep learning models across various domains.