This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each epoch to the real manifolds representing the real data. To quantify a manifold, we study the intrinsic dimensions and topological features of the manifold learned by the ML model, how these metrics change as we continue to train the model, and whether these metrics convergence over the course of training to the metrics of the real data manifold.

翻译：本文展示了我们量化机器学习模型（实验中选用生成对抗网络模型）训练过程中所学流形的实验。我们将每一训练周期学习的流形与代表真实数据的真实流形进行比较。为量化流形，我们研究了机器学习模型所学流形的内在维度和拓扑特征，这些指标随模型持续训练的变化趋势，以及这些指标在训练过程中是否收敛于真实数据流形的指标。