Once for All: Train One Network and Specialize it for Efficient Deployment
Abstract: ….In this work, we introduce Once for All (OFA) for efficient neural network design to handle many deployment scenarios, a new methodology that decouples model training from architecture search. Instead of training a specialized model for each case, we propose to train a once-for-all network that supports diverse architectural settings (depth, width, kernel size, and resolution). Given a deployment scenario, we can later search a specialized sub-network by selecting from the once-for-all network without training. As such, the training cost of specialized models is reduced from O(N) to O(1). However, it's challenging to prevent interference between many sub-networks. Therefore we propose the progressive shrinking algorithm, which is capable of training a once-for-all network to support more than 1019 sub-networks while maintaining the same accuracy as independently trained networks, saving the non-recurring engineering (NRE) cost….
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Abstract: We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information. Our model is based on a conditional generative adversarial network, generating images considering the original pose and image background. The conditional information enables us to generate highly realistic faces with a seamless transition between the generated face and the existing background. Furthermore, we introduce a diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds. Finally, we present experimental results reflecting the capability of our model to anonymize images while preserving the data distribution, making the data suitable for further training of deep learning models. As far as we know, no other solution has been proposed that guarantees the anonymization of faces while generating realistic images.