{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","arxiv_id":"1602.07261","date":"2016-02-23","proceeding":null,"authors":["Christian Szegedy","Sergey Ioffe","Vincent Vanhoucke","Alex Alemi"],"abstract":"Very deep convolutional networks have been central to the largest advances in\nimage recognition performance in recent years. One example is the Inception\narchitecture that has been shown to achieve very good performance at relatively\nlow computational cost. Recently, the introduction of residual connections in\nconjunction with a more traditional architecture has yielded state-of-the-art\nperformance in the 2015 ILSVRC challenge; its performance was similar to the\nlatest generation Inception-v3 network. This raises the question of whether\nthere are any benefit in combining the Inception architecture with residual\nconnections. Here we give clear empirical evidence that training with residual\nconnections accelerates the training of Inception networks significantly. There\nis also some evidence of residual Inception networks outperforming similarly\nexpensive Inception networks without residual connections by a thin margin. We\nalso present several new streamlined architectures for both residual and\nnon-residual Inception networks. These variations improve the single-frame\nrecognition performance on the ILSVRC 2012 classification task significantly.\nWe further demonstrate how proper activation scaling stabilizes the training of\nvery wide residual Inception networks. With an ensemble of three residual and\none Inception-v4, we achieve 3.08 percent top-5 error on the test set of the\nImageNet classification (CLS) challenge","url_abs":"http://arxiv.org/abs/1602.07261v2","url_pdf":"http://arxiv.org/pdf/1602.07261v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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