Custom Implementation of Non-deep Networks
arXiv:2110.07641
Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun
Official Repository https://github.com/imankgoyal/NonDeepNetworks
Overview: Depth is the hallmark of DNNs. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performing ``non-deep" neural networks? We show that it is. We show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and 81% on CIFAR100. We also show that a network with a low-depth (12) backbone can achieve an AP of 48% on MS-COCO.
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