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Data augmentation for deep learning benefits model training, imagetransformation, medical imaging analysis and many other fields. Many existingmethods generate new samples from a parametric distribution, like the Gaussian,with little attention to generate samples along the data manifold in either theinput or feature space. In this paper, we verify that there are theoretical andpractical advantages of using the principal manifold hidden in the featurespace than the Gaussian distribution. We then propose a novel trajectory-awareprincipal manifold framework to restore the manifold backbone and generatesamples along a specific trajectory. On top of the autoencoder architecture, wefurther introduce an intrinsic dimension regularization term to make themanifold more compact and enable few-shot image generation. Experimentalresults show that the novel framework is able to extract more compact manifoldrepresentation, improve classification accuracy and generate smoothtransformation among few samples.

Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation