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Rethinking the Inception Architecture for Computer Vision
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for
doi:10.1109/cvpr.2016.308
dblp:conf/cvpr/SzegedyVISW16
fatcat:y7t4u7ifdfd7nhb2blj3k237ia