Bottleneck Model

Bottleneck models are a class of machine learning models designed to improve the interpretability of complex systems by forcing information to pass through a constrained "bottleneck" layer. Current research focuses on enhancing both the accuracy and explainability of these models, exploring architectures like Concept Bottleneck Models (CBMs) which leverage concepts (e.g., features or attributes) to bridge the gap between input data and output predictions, and incorporating cross-modal information (e.g., text and images) to guide concept learning. This work is significant because it addresses the "black box" nature of many deep learning models, leading to more trustworthy and understandable AI systems with applications ranging from medical image analysis to visual question answering.