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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Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture.
TL;DR: Wang et al. as discussed by the authors investigated a deep learning-based dose prediction model, Hierarchically Densely Connected U-Net, based on two highly popular network architectures: U-net and DenseNet.
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Fine-grained leukocyte classification with deep residual learning for microscopic images.
TL;DR: Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly.
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State of the Art in Defect Detection Based on Machine Vision
TL;DR: A detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms.
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Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
TL;DR: A new algorithm, called Partially adaptive momentum estimation method (Padam), is designed, which unifies the Adam/Amsgrad with SGD to achieve the best from both worlds and can maintain fast convergence rate as Adam and Amsgrad while generalizing as well as SGD in training deep neural networks.
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A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring
TL;DR: In this article, a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures was used to detect printing defects such as delamination and splatter with an accuracy of 96.80%.
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