Trajectory-aware Principal Manifold Framework for Data Augmentation and
  Image Generation
Elvis Han Cui Bingbin Li Yanan Li Weng Kee Wong Donghui Wang
Data augmentation for deep learning benefits model training, image
transformation, medical imaging analysis and many other fields. Many existing
methods generate new samples from a parametric distribution, like the Gaussian,
with little attention to generate samples along the data manifold in either the
input or feature space. In this paper, we verify that there are theoretical and
practical advantages of using the principal manifold hidden in the feature
space than the Gaussian distribution. We then propose a novel trajectory-aware
principal manifold framework to restore the manifold backbone and generate
samples along a specific trajectory. On top of the autoencoder architecture, we
further introduce an intrinsic dimension regularization term to make the
manifold more compact and enable few-shot image generation. Experimental
results show that the novel framework is able to extract more compact manifold
representation, improve classification accuracy and generate smooth
transformation among few samples.