Detecting Overfitting of Deep Generative Networks via Latent Recovery

Abstract

State of the art deep generative networks have achieved such realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We argue this is not sufficient and motivates studying overfitting of deep generators with more scrutiny. We address this question by i) showing how simple losses are highly effective at reconstructing images for deep generators ii) analyzing the statistics of reconstruction errors for training versus validation images. Using this methodology, we show that pure GAN models appear to generalize well, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods. We also show that standard GAN evaluation metrics fail to capture memorization for some deep generators. Finally, we note the ramifications of memorization on data privacy. Considering the already widespread application of generative networks, we provide a step in the right direction towards the important yet incomplete picture of generative overfitting.