Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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
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DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
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Material recognition in the wild with the Materials in Context Database
TL;DR: The Materials in Context Database (MINC) as mentioned in this paper is a large-scale, open dataset of materials in the wild, and combine this dataset with deep learning to achieve material recognition and segmentation of images from the wild.
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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.
TL;DR: This work is the first to study subcortical structure segmentation on such large‐scale and heterogeneous data and yielded segmentations that are highly consistent with a standard atlas‐based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps.
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Brain tumor classification for MR images using transfer learning and fine-tuning.
Zar Nawab Khan Swati,Qinghua Zhao,Muhammad Kabir,Farman Ali,Zakir Ali,Saeed Ahmed,Jianfeng Lu +6 more
TL;DR: A block-wise fine-tuning strategy based on transfer learning is proposed that outperforms state-of-the-art classification on the CE-MRI dataset and can achieve average accuracy of 94.82% under five-fold cross-validation.
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Material recognition in the wild with the Materials in Context Database
TL;DR: A new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), is introduced, and convolutional neural networks are trained for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images.
References
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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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Fully Convolutional Networks for Semantic Segmentation
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Journal ArticleDOI
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.
Journal ArticleDOI
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.