Two Applications of Manifold Regularization in Deep Learning Architectures
Weixuan Yuan, Chengyuan Zhang, Wenrui Song, Siyi Yang · Journal of Physics Conference Series · 2023
Abstract With the rapid development of machine learning today, deep learning has shown great advantages in many fields. Due to the high cost of datasets with labels, its branch semi-supervised learning has become an important research area. However, existing semi-supervised learning methods perform poorly on extremely small datasets, for instance, only one labeled example for each class. Hence, we introduce methods that apply manifold approaches to DL(Deep Learning) and frameworks that implement the idea. Potential application scenarios were analyzed based on experiments on synthetic and real-world experiments.