Recently introduced generative adversarial network (GAN) has been shown
numerous promising results to generate realistic samples. The essential task of
GAN is to control the features of samples generated from a random distribution.
While the current GAN structures, such as conditional GAN, successfully
generate samples with desired major features, they often fail to produce
detailed features that bring specific differences among samples. To overcome
this limitation, here we propose a controllable GAN (ControlGAN) structure. By
separating a feature classifier from a discriminator, the generator of
ControlGAN is designed to learn generating synthetic samples with the specific
detailed features. Evaluated with multiple image datasets, ControlGAN shows a
power to generate improved samples with well-controlled features. Furthermore,
we demonstrate that ControlGAN can generate intermediate features and opposite
features for interpolated and extrapolated input labels that are not used in
the training process. It implies that ControlGAN can significantly contribute
to the variety of generated samples.

本文提出了一种新型的 GAN 结构 ControlGAN，通过将特征分类器与鉴别器分离，设计了一个可以控制样本特定详细特征的生成器，并使用多个图像数据集进行了评估，展示了 ControlGAN 在生成具有良好控制特性的改进样本方面的潜力。此外，我们证明了 ControlGAN 可以为插值和外推输入标签生成中间特征和相反特征，这表明 ControlGAN 可以显着增加生成样本的多样性。