B Co

B-cos networks represent a novel approach to enhancing the interpretability of deep neural networks, particularly within computer vision. Research focuses on replacing standard linear transformations with a B-cos transformation, which promotes weight-input alignment and allows the entire network's computations to be summarized by a single, interpretable linear transform. This method has been successfully integrated into various architectures, including Vision Transformers (ViTs), Swin Transformers, and convolutional neural networks (CNNs), demonstrating improved interpretability without significant performance loss on benchmark datasets like ImageNet. The resulting increased transparency offers significant potential for advancing trust and understanding in complex deep learning models, particularly in high-stakes applications like medical image analysis.