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Resnet in Resnet: Generalizing Residual Architectures
Sasha Targ, Diogo Almeida, Kevin Lyman
Feb 18, 2016 (modified: Feb 18, 2016)ICLR 2016 workshop submissionreaders: everyone
Abstract:ResNets have recently achieved state-of-the-art results on challenging computer vision tasks. In this paper, we create a novel architecture that improves ResNets by adding the ability to forget and by making the residuals more expressive, yielding excellent results. ResNet in ResNet outperforms architectures with similar amounts of augmentation on CIFAR-10 and establishes a new state-of-the-art on CIFAR-100.
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