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Introducing Capsule Networks and How They Work
Introduction
Deep learning has led to groundbreaking achievements in fields ranging from image recognition and language modeling to speech processing and robotics. Convolutional Neural Networks (CNNs), in particular, have dominated visual tasks, enabling machines to classify and localize objects with remarkable accuracy. Yet despite these successes, CNNs still have critical limitations in understanding the intrinsic geometry of objects and how parts of an object relate to the whole — especially when objects undergo complex transformations such as rotation, scaling, or viewpoint changes.
To address these issues, Geoffrey Hinton and collaborators introduced Capsule Networks (CapsNets) — an approach that models a deeper, more structured representation of visual data. Capsules encode not just the probability of an object’s presence but also the parameters of its pose (e.g., position, orientation, scale). This article provides an extensive, in-depth exploration of Capsule Networks, detailing their motivation, underlying theory, architecture, training process, current challenges, and real-world use cases. We will also compare CapsNets to other deep learning architectures, highlight their unique advantages, and discuss where this technology is heading.
