It is no secret that deep neural networks (DNNs) can achieve state-of-the-art performance in a wide range of complicated tasks. DNN models such as BigGAN, BERT, and GPT 2.0 have proved the high potential of deep learning. Deploying DNNs on mobile devices, consumer devices, drones and vehicles however remains a bottleneck for researchers. For such practical, on-device scenarios, DNNs must have a smaller footprint.
AttoNets: Compact and Efficient DNNs Realized via Human-Machine Collaborative
Thursday, March 21, 2019