Convolutional Networks with Dense Connectivity

IEEE TRANSACTIONS ON P A TTERN ANAL YSIS AND MACHINE INTELLIGENCE 1 Convolutional Networks with Dense Connectivity Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten and Kilian Q. Weinberger Abstract--Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections--one between each layer and its subsequent layer--our network has L(L 1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, encourage feature reuse and substantially improve parameter efficiency . We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less parameters and computation to achieve high performance. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I NTRODUCTION C ONVOLUTIONALneural networks (CNNs) have become the dominant machine learning approach for visual object recognition. Although they were originally introduced over 20 years ago [1], improvements in computer hardware and network structure have enabled the training of truly deep CNNs only recently. The original LeNet5 [2] consisted of 5 layers, VGG featured 19 [3], and thanks to the skip/shortcut connections, Highway Networks [4] and Residual Networks (ResNets) [5] have surpassed the 100-layer barrier. As CNNs become increasingly deep, a new research problem emerges: information about the input or gradient that passes through many layers it can vanish and "wash out" by the time it reaches the end (or beginning) of the network. Many recent publications address this problem. For example, Rectified Linear Unites (ReLU) [6] avoid gradient saturation, batch-normalization [7] reduces covariate shift across layers by re-scaling the outputs of its previous layer. ResNets [5] and Highway Networks [4] bypass signal from one layer to the next via identity connections. Stochastic depth [8] shortens ResNets by randomly dropping layers during training to allow better information and gradient flow.