Geometric deep learning refers to a category of techniques that aim to generalize deep learning methods to non-Euclidean domains such as graphs and manifolds. These techniques are designed to handle data with geometric structure, and they’re often used in various fields including computer vision, physics, chemistry, and network analysis.
The main difference between geometric deep learning and traditional deep learning lies in the data structure. While traditional methods usually work with grid-like data (e.g., images), geometric deep learning deals with more complex structures where the relationships between data points might not be regular.
Graph Neural Networks (GNNs), for example, are a popular type of geometric deep learning model. They are specifically designed to work with graph-structured data and can capture the complex relationships between nodes in a graph.
Geometric deep learning has found applications in various fields, such as drug discovery (where molecules can be represented as graphs), social network analysis (where people and their connections form a graph), and even in 3D object recognition (where the shape of an object can be represented using manifold structures).
The development of specialized algorithms for these non-Euclidean domains is an active area of research and continues to contribute to advancements in many scientific and industrial fields.