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Very deep convolutional networks have been central to the largest advances inimage recognition performance in recent years. One example is the Inceptionarchitecture that has been shown to achieve very good performance at relativelylow computational cost. Recently, the introduction of residual connections inconjunction with a more traditional architecture has yielded state-of-the-artperformance in the 2015 ILSVRC challenge; its performance was similar to thelatest generation Inception-v3 network. This raises the question of whetherthere are any benefit in combining the Inception architecture with residualconnections. Here we give clear empirical evidence that training with residualconnections accelerates the training of Inception networks significantly. Thereis also some evidence of residual Inception networks outperforming similarlyexpensive Inception networks without residual connections by a thin margin. Wealso present several new streamlined architectures for both residual andnon-residual Inception networks. These variations improve the single-framerecognition performance on the ILSVRC 2012 classification task significantly.We further demonstrate how proper activation scaling stabilizes the training ofvery wide residual Inception networks. With an ensemble of three residual andone Inception-v4, we achieve 3.08 percent top-5 error on the test set of theImageNet classification (CLS) challenge

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning