Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy computation and storage cost. Traditional network compression methods focus on visually recognition tasks, but never deal with generation tasks. Inspired by knowledge distillation, a student generator of fewer parameters is trained by inheriting the low-level and high-level information from the original heavy teacher generator. To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators. An adversarial learning process is therefore established to optimize student generator and student discriminator. Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.

Recent rapid development of machine learning is largely due to algorithmic breakthroughs, computation resource development, and especially the access to a large amount of training data. However, though data sharing has the great potential of improving machine learning models and enabling new applications, there have been increasing concerns about the privacy implications of data collection. In this work, we present a novel approach for training differentially private data generator G-PATE. The generator can be used to produce synthetic datasets with strong privacy guarantee while preserving high data utility. Our approach leverages generative adversarial nets (GAN) to generate data and protect data privacy based on the Private Aggregation of Teacher Ensembles (PATE) framework. Our approach improves the use of privacy budget by only ensuring differential privacy for the generator, which is the part of the model that actually needs to be published for private data generation. To achieve this, we connect a student generator with an ensemble of teacher discriminators. We also propose a private gradient aggregation mechanism to ensure differential privacy on all the information that flows from the teacher discriminators to the student generator. We empirically show that the G-PATE significantly outperforms prior work on both image and non-image datasets.