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We re-evaluate the\nstate of the art for object recognition from small images with convolutional\nnetworks, questioning the necessity of different components in the pipeline. We\nfind that max-pooling can simply be replaced by a convolutional layer with\nincreased stride without loss in accuracy on several image recognition\nbenchmarks. Following this finding -- and building on other recent work for\nfinding simple network structures -- we propose a new architecture that\nconsists solely of convolutional layers and yields competitive or state of the\nart performance on several object recognition datasets (CIFAR-10, CIFAR-100,\nImageNet). To analyze the network we introduce a new variant of the\n\"deconvolution approach\" for visualizing features learned by CNNs, which can be\napplied to a broader range of network structures than existing approaches.","url_abs":"http://arxiv.org/abs/1412.6806v3","url_pdf":"http://arxiv.org/pdf/1412.6806v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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