Interpretable Layer

Interpretable layers in neural networks aim to enhance the transparency and understandability of deep learning models, addressing the "black box" problem. Current research focuses on developing novel layer architectures, such as simplicial maps and piecewise linear functions, and integrating them into existing models like CNNs and transformers, often using techniques like attention mechanisms and class activation mapping to highlight relevant features. This work is significant because it improves trust and allows for better debugging and refinement of complex models, particularly in high-stakes applications like legal decision-making and medical diagnosis where understanding model reasoning is crucial.