Spatial Entropy

Spatial entropy, a measure of the randomness or uncertainty in the spatial distribution of data, is increasingly used as a tool in various fields, primarily to improve model performance and efficiency. Current research focuses on incorporating spatial entropy into loss functions for image processing tasks (e.g., low-light enhancement), developing entropy-based methods for causal inference and neural network pruning, and leveraging it as an inductive bias in deep learning architectures like Vision Transformers. These applications demonstrate the growing importance of spatial entropy as a powerful tool for enhancing the accuracy, efficiency, and interpretability of machine learning models across diverse domains.