Kurtosis Concentration

Kurtosis concentration (KC) refers to the relative uniformity of kurtosis—a statistical measure of data distribution's "tailedness"—across different frequency bands or scales of a signal or image. Current research focuses on leveraging KC to improve the quality of generated images in diffusion models and enhance signal processing techniques, such as speech enhancement and time-series forecasting, by incorporating KC-based loss functions into deep learning architectures. This approach aims to improve model performance, particularly in handling outliers or rare events, leading to more robust and accurate results in various applications, including image generation, speech recognition, and predictive modeling.