Image Manifold

Image manifolds represent the underlying low-dimensional structure of high-dimensional image data, aiming to capture the inherent relationships between images and facilitate tasks like classification, generation, and editing. Current research focuses on learning and manipulating these manifolds using various techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and graph neural networks (GNNs), often incorporating concepts like disentanglement and geometric preservation. Understanding and effectively modeling image manifolds is crucial for advancing numerous computer vision applications, improving robustness to adversarial attacks, and enabling more sophisticated image manipulation and generation techniques.