Wide Residual Networks
This addresses the problem of inefficient training and diminishing returns in deep learning architectures for computer vision researchers and practitioners, offering a more effective alternative to very deep networks.
The paper tackled the diminishing feature reuse and slow training of very deep residual networks by proposing wide residual networks (WRNs) that decrease depth and increase width, achieving new state-of-the-art results on datasets like CIFAR, SVHN, COCO, and significant improvements on ImageNet, with a 16-layer-deep WRN outperforming thousand-layer-deep networks in accuracy and efficiency.
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