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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Journal ArticleDOI
DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.
Ramsey M. Wehbe,Jiayue Sheng,Shinjan Dutta,Siyuan Chai,Amil Dravid,Semih Barutcu,Yunan Wu,Donald R. Cantrell,Nicholas Xiao,Bradley D. Allen,Gregory A. MacNealy,Hatice Savas,Rishi Agrawal,Nishant D. Parekh,Aggelos K. Katsaggelos +14 more
TL;DR: DeepCOVID-XR, an artificial intelligence algorithm, detected coronavirus disease 2019 on chest radiographs with a performance similar to that of experienced thoracic radiologists in consensus.
Proceedings ArticleDOI
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
TL;DR: VoVNet also outperforms widely used ResNet backbone with faster speed and better energy efficiency and the small object detection performance has been significantly improved over DenseNet and ResNet.
Journal ArticleDOI
Detection of rice plant diseases based on deep transfer learning.
TL;DR: The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection.
Journal ArticleDOI
Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition
TL;DR: A multi-scale multi-view feature aggregation (MSMVFA) scheme that can aggregate high-level semantic features, mid-level attribute features, and deep visual features into a unified representation for food recognition and achieves state-of-the-art recognition performance on three popular large-scale food benchmark datasets.
Posted Content
Classification-Reconstruction Learning for Open-Set Recognition
TL;DR: This work utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy, and outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).