  For deep learning applications, the massive data development (e.g.,
collecting, labeling), which is an essential process in building practical
applications, still incurs seriously high costs. In this work, we propose an
effective data augmentation method based on generative adversarial networks
(GANs), called Domain Fusion. Our key idea is to import the knowledge contained
in an outer dataset to a target model by using a multi-domain learning GAN. The
multi-domain learning GAN simultaneously learns the outer and target dataset
and generates new samples for the target tasks. The simultaneous learning
process makes GANs generate the target samples with high fidelity and variety.
As a result, we can obtain accurate models for the target tasks by using these
generated samples even if we only have an extremely low volume target dataset.
We experimentally evaluate the advantages of Domain Fusion in image
classification tasks on 3 target datasets: CIFAR-100, FGVC-Aircraft, and Indoor
Scene Recognition. When trained on each target dataset reduced the samples to
5,000 images, Domain Fusion achieves better classification accuracy than the
data augmentation using fine-tuned GANs. Furthermore, we show that Domain
Fusion improves the quality of generated samples, and the improvements can
contribute to higher accuracy.
