Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
Citations
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A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
TL;DR: The proposed framework conducts three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, gliomas, and pituitary and achieves highest accuracy up to 98.69 in terms of classification and detection.
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FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates
Yijin Guan,Hao Liang,Ningyi Xu,Wenqiang Wang,Shaoshuai Shi,Xi Chen,Guangyu Sun,Wei Zhang,Jason Cong +8 more
TL;DR: FP-DNN (Field Programmable DNN), an end-to-end framework that takes TensorFlow-described DNNs as input, and automatically generates the hardware implementations on FPGA boards with RTL-HLS hybrid templates, is proposed.
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Image De-raining Using a Conditional Generative Adversarial Network
TL;DR: Zhang et al. as mentioned in this paper proposed a novel single image de-raining method called Image De-raining Conditional General Adversarial Network (ID-CGAN), which considers quantitative, visual and also discriminative performance into the objective function.
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Medical Image Analysis using Convolutional Neural Networks: A Review
TL;DR: A comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented and the challenges and potential of these techniques are also highlighted.
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi,Jose Caballero,Ferenc Huszar,Johannes Totz,Andrew Peter Aitken,Rob Bishop,Daniel Rueckert,Zehan Wang +7 more
TL;DR: In this paper, the feature maps are extracted in the LR space and an efficient sub-pixel convolution layer is introduced to upscale the final LR feature maps into the HR output, which reduces the computational complexity of the overall SR operation.
References
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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.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Posted Content
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Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
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The Pascal Visual Object Classes Challenge: A Retrospective
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.