From Parts to Whole: Capsule Networks for Interpretable and Transparent AI

Are you curious about what lies beyond traditional Deep Convolutional Neural Networks (CNNs)? Deep learning has changed how machines perceive the world, but traditional CNNs often miss the bigger picture. They recognize what is present, yet struggle to understand how parts relate to a whole. Capsule Networks (CapsNets) offer a fresh perspective: a neural architecture that captures spatial hierarchies and relationships, which provides more transparent and human-understandable representations Join us to explore how Capsule Networks work, why they matter, and how they bring us closer to truly interpretable AI. Discover their advantages over CNNs, their role in Explainable AI, and the growing research shaping the future of transparent deep learning.