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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Proceedings ArticleDOI
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
Zheng Tang,Milind Naphade,Stan Birchfield,Jonathan Tremblay,William Hodge,Ratnesh Kumar,Shuo Wang,Xiaodong Yang +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed a Pose-Aware Multi-Task Re-Identification (PAMTRI) framework, which overcomes viewpoint-dependency by explicitly reasoning about vehicle pose and shape via keypoints, heatmaps and segments from pose estimation.
Proceedings ArticleDOI
Deep Graph Convolutional Encoders for Structured Data to Text Generation
TL;DR: This paper proposes an alternative encoder based on graph convolutional networks that directly exploits the input structure and reports results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.
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Multi-disease prediction based on deep learning: A survey
TL;DR: Some basic deep learning frameworks and some common diseases are introduced, and the deep learning prediction methods corresponding to different diseases are summarized, to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development.
Journal ArticleDOI
End-to-end design of wearable sensors
H. Ceren Ates,Peter Q. Nguyen,Laura Gonzalez-Macia,Eden Morales-Narváez,Firat Güder,J. J. Collins,Can Dincer +6 more
TL;DR: Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner as mentioned in this paper .
Posted Content
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
TL;DR: DFANet as discussed by the authors proposes an efficient CNN architecture based on multi-scale feature propagation, which substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability.
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