Entropy Loss

Entropy loss functions are being actively explored to improve the performance and interpretability of deep learning models, particularly in challenging domains like object detection and outlier detection. Current research focuses on adapting entropy-based losses for various model architectures, including autoencoders and convolutional neural networks, often incorporating them into hybrid loss functions or employing them for early stopping criteria to enhance training efficiency and robustness. These advancements aim to address limitations in existing deep learning approaches, such as the "black box" nature of many models and their susceptibility to noisy or out-of-distribution data, leading to more reliable and explainable AI systems across diverse applications.