ملخص: Artificial data synthesis is currently a well-studied topic with useful applications in data science, computer vision, graphics, and many other fields. Generating realistic data is especially challenging, since human perception is highly sensitive to non-realistic appearance. In recent times, new levels of realism have been achieved by advances in GAN training procedures and architectures. These successful models, however, are tuned mostly for use with regularly sampled data such as images, audio, and video. Despite the successful application of the architecture on these types of media, applying the same tools to geometric data poses a far greater challenge. The study of geometric deep learning is still a debated issue within the academic community, as the lack of intrinsic parametrization inherent to geometric objects prohibits the direct use of convolutional filters, a main building block of today's machine learning systems.

لغة: إنجليزية