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First published: 2016/08/09 (8 years ago)
Abstract: Residual networks family with hundreds or even thousands of layers dominate major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping, in particular, adding level-wise shortcut connections upon original residual networks, to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (Pre-ResNets and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4 models achieve new state-of-the-art results on CIFAR-10, CIFAR-100 and SVHN, with test errors 3.77%, 19.73% and 1.59% respectively. These results outperform 1001-layer Pre-ResNets by 18.4% on CIFAR-10 and 13.1% on CIFAR-100.