2019/11/19 by Bing Xu, Andrew Tulloch, Xu, Bing +7
Computer Science · Neuroscience · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.1911.08609
arxiv created 2019/11/19 · openalex publication_date 2019/11/19 · arxiv updated 2019/11/21 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
We propose a new building block, IdleBlock, which naturally prunes connections within the block. To fully utilize the IdleBlock we break the tradition of monotonic design in state-of-the-art networks, and introducing hybrid composition with IdleBlock. We study hybrid composition on MobileNet v3 and EfficientNet-B0, two of the most efficient networks. Without any neural architecture search, the deeper "MobileNet v3" with hybrid composition design surpasses possibly all state-of-the-art image recognition network designed by human experts or neural architecture search algorithms. Similarly, the hybridized EfficientNet-B0 networks are more efficient than previous state-of-the-art networks with similar computation budgets. These results suggest a new simpler and more efficient direction for network design and neural architecture search.