Wide Residual Networks
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.
Also cited · not yet reviewed (3)
- VGG2014 · cited 4דConvolutional neural networks have seen a gradual increase of the number of layers in the last few years, starting from AlexNet [Krizhevsky et al.(2012a)Krizhevsky, Sutskever, and Hinton], VGG [Simonyan and Zisserman(201…”From this paper · §Introduction
- ResNet2015 · cited 3דConvolutional neural networks have seen a gradual increase of the number of layers in the last few years, starting from AlexNet [Krizhevsky et al.(2012a)Krizhevsky, Sutskever, and Hinton], VGG [Simonyan and Zisserman(201…”From this paper · §Introduction
- GoogLeNet (Inception)2014 · cited 2דConvolutional neural networks have seen a gradual increase of the number of layers in the last few years, starting from AlexNet [Krizhevsky et al.(2012a)Krizhevsky, Sutskever, and Hinton], VGG [Simonyan and Zisserman(201…”From this paper · §Introduction
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- EfficientNet2019 · cited 5×, 2 in Method“However, deeper networks are also more difficult to train due to the vanishing gradient problem (Zagoruyko & Komodakis 2016).”From EfficientNet · §Compound Model Scaling
- BYOL2020 · cited 1×, 1 in Method“We also use deeper (5050, 101101, 152152 and 200200 layers) and wider (from 1×1\times to 4×4\times) ResNets, as in [67, 48, 8].”From BYOL · §Method
Abstract
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. Our code and models are available at https://github.com/szagoruyko/wide-residual-networks