Synthesizing Informative Training Samples with GAN
This addresses the dataset condensation problem for machine learning practitioners when real data is expensive or infeasible to obtain, though it is an incremental improvement over existing GAN methods.
The paper tackles the problem of GAN-generated images being less informative than real training samples for training deep neural networks, proposing IT-GAN to synthesize informative training samples that lead to faster learning and better performance, with experiments showing improved results.
Remarkable progress has been achieved in synthesizing photo-realistic images with generative adversarial networks (GANs). Recently, GANs are utilized as the training sample generator when obtaining or storing real training data is expensive even infeasible. However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks. In this paper, we propose a novel method to synthesize Informative Training samples with GAN (IT-GAN). Specifically, we freeze a pre-trained GAN model and learn the informative latent vectors that correspond to informative training samples. The synthesized images are required to preserve information for training deep neural networks rather than visual reality or fidelity. Experiments verify that the deep neural networks can learn faster and achieve better performance when being trained with our IT-GAN generated images. We also show that our method is a promising solution to dataset condensation problem.