Information Theoretic Loss

Information-theoretic loss functions are emerging as a powerful tool in deep learning, aiming to improve model robustness and generalization by optimizing the information content of learned representations. Current research focuses on applying these losses within various architectures, including prototypical models and deep networks, to address challenges like noisy labels, zero-shot learning, and out-of-distribution detection. This approach leverages concepts like mutual information and entropy to encourage both intra-class compactness and inter-class separability in the feature space, leading to more effective and reliable models across diverse applications. The resulting improvements in model performance and robustness have significant implications for various fields, including computer vision and anomaly detection.