  We introduce a challenging training scheme of conditional GANs, called
open-set semi-supervised image generation, where the training dataset consists
of two parts: (i) labeled data and (ii) unlabeled data with samples belonging
to one of the labeled data classes, namely, a closed-set, and samples not
belonging to any of the labeled data classes, namely, an open-set. Unlike the
existing semi-supervised image generation task, where unlabeled data only
contain closed-set samples, our task is more general and lowers the data
collection cost in practice by allowing open-set samples to appear. Thanks to
entropy regularization, the classifier that is trained on labeled data is able
to quantify sample-wise importance to the training of cGAN as confidence,
allowing us to use all samples in unlabeled data. We design OSSGAN, which
provides decision clues to the discriminator on the basis of whether an
unlabeled image belongs to one or none of the classes of interest, smoothly
integrating labeled and unlabeled data during training. The results of
experiments on Tiny ImageNet and ImageNet show notable improvements over
supervised BigGAN and semi-supervised methods. Our code is available at
https://github.com/raven38/OSSGAN.
