Variational Information Bottleneck

Variational Information Bottleneck (VIB) is a machine learning framework aiming to learn compressed, yet informative, data representations by minimizing redundant information while maximizing relevant information for a specific task. Current research focuses on applying VIB to diverse areas, including model compression (e.g., pruning transformers), multimodal learning (e.g., entity alignment, medical image segmentation), and improving robustness in various applications (e.g., authorship attribution, speech anti-spoofing). This approach offers significant advantages by enhancing model efficiency, generalization, and robustness, impacting fields ranging from natural language processing and computer vision to personalized recommendations and medical image analysis.