  The great success achieved by deep neural networks attracts increasing
attention from the manufacturing and healthcare communities. However, the
limited availability of data and high costs of data collection are the major
challenges for the applications in those fields. We propose in this work AISEL,
an active image synthesis method for efficient labeling to improve the
performance of the small-data learning tasks. Specifically, a complementary
AISEL dataset is generated, with labels actively acquired via a physics-based
method to incorporate underlining physical knowledge at hand. An important
component of our AISEL method is the bidirectional generative invertible
network (GIN), which can extract interpretable features from the training
images and generate physically meaningful virtual images. Our AISEL method then
efficiently samples virtual images not only further exploits the uncertain
regions, but also explores the entire image space. We then discuss the
interpretability of GIN both theoretically and experimentally, demonstrating
clear visual improvements over the benchmarks. Finally, we demonstrate the
effectiveness of our AISEL framework on aortic stenosis application, in which
our method lower the labeling cost by $90\%$ while achieving a $15\%$
improvement in prediction accuracy.
