Densely Connected Convolutional Networks
Gao Huang,Zhuang Liu,Laurens van der Maaten,Kilian Q. Weinberger +3 more
- pp 2261-2269
TLDR
DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.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 memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.read more
Citations
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Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs
Chi-Tung Cheng,Tsung-Ying Ho,Lee Tao-Yi,Chih-Chen Chang,Ching-Cheng Chou,Chih-Chi Chen,I-Fang Chung,Chien-Hung Liao +7 more
TL;DR: A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions and good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system.
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Faster gaze prediction with dense networks and Fisher pruning
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Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
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YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Chuyin Li,Lu Li,Hongliang Jiang,Kaiheng Weng,Yifei Geng,Lin Li,Zaidan Ke,Qingyuan Li,Meng Cheng,Weiqiang Nie,Yiduo Li,Yufei Liang,Linyuan Zhou,Xiaoming Xu,Xiangxiang Chu,Xiaoming Wei,Xiaolin Wei +16 more
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