  Deep learning approaches deliver state-of-the-art performance in recognition
of spatiotemporal human motion data. However, one of the main challenges in
these recognition tasks is limited available training data. Insufficient
training data results in over-fitting and data augmentation is one approach to
address this challenge. Existing data augmentation strategies based on scaling,
shifting and interpolating offer limited generalizability and typically require
detailed inspection of the dataset as well as hundreds of GPU hours for
hyperparameter optimization. In this paper, we present a novel automatic data
augmentation model, the Imaginative Generative Adversarial Network (GAN), that
approximates the distribution of the input data and samples new data from this
distribution. It is automatic in that it requires no data inspection and little
hyperparameter tuning and therefore it is a low-cost and low-effort approach to
generate synthetic data. We demonstrate our approach on small-scale
skeleton-based datasets with a comprehensive experimental analysis. Our results
show that the augmentation strategy is fast to train and can improve
classification accuracy for both conventional neural networks and
state-of-the-art methods.
