  Unsupervised image-to-image translation has gained considerable attention due
to the recent impressive progress based on generative adversarial networks
(GANs). However, previous methods often fail in challenging cases, in
particular, when an image has multiple target instances and a translation task
involves significant changes in shape, e.g., translating pants to skirts in
fashion images. To tackle the issues, we propose a novel method, coined
instance-aware GAN (InstaGAN), that incorporates the instance information
(e.g., object segmentation masks) and improves multi-instance transfiguration.
The proposed method translates both an image and the corresponding set of
instance attributes while maintaining the permutation invariance property of
the instances. To this end, we introduce a context preserving loss that
encourages the network to learn the identity function outside of target
instances. We also propose a sequential mini-batch inference/training technique
that handles multiple instances with a limited GPU memory and enhances the
network to generalize better for multiple instances. Our comparative evaluation
demonstrates the effectiveness of the proposed method on different image
datasets, in particular, in the aforementioned challenging cases. Code and
results are available in https://github.com/sangwoomo/instagan
