Densely Connected Convolutional Networks

TL;DR

DenseNet introduces a densely connected convolutional architecture where each layer connects to all previous layers, improving feature reuse, gradient flow, and reducing parameters, leading to superior performance on benchmark datasets.

Contribution

This paper proposes DenseNet, a novel convolutional network architecture with dense connections, enhancing feature propagation and reducing parameters compared to traditional models.

Findings

Outperforms state-of-the-art on CIFAR-10, CIFAR-100, SVHN, ImageNet

Reduces number of parameters significantly

Improves accuracy and training efficiency

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, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. 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 computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet .