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ImageNet
Known as:
ImageNet Large Scale Visual Recognition Challenge
, ImageNet competition
The ImageNet project is a large visual database designed for use in visual object recognition software research. As of 2016, over ten million URLs of…
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Computer vision
Database
Deep learning
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2019
Highly Cited
2019
Self-Training With Noisy Student Improves ImageNet Classification
Qizhe Xie
,
E. Hovy
,
Minh-Thang Luong
,
Quoc V. Le
Computer Vision and Pattern Recognition
2019
Corpus ID: 207853355
We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of…
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Highly Cited
2018
Highly Cited
2018
Rethinking ImageNet Pre-Training
Kaiming He
,
Ross B. Girshick
,
Piotr Dollár
IEEE International Conference on Computer Vision
2018
Corpus ID: 53739271
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained…
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Highly Cited
2018
Highly Cited
2018
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos
,
Patricia Rubisch
,
Claudio Michaelis
,
M. Bethge
,
Felix Wichmann
,
Wieland Brendel
International Conference on Learning…
2018
Corpus ID: 54101493
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations…
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Highly Cited
2017
Highly Cited
2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal
,
Piotr Dollár
,
+6 authors
Kaiming He
ArXiv
2017
Corpus ID: 13905106
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in…
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Highly Cited
2016
Highly Cited
2016
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Mohammad Rastegari
,
Vicente Ordonez
,
Joseph Redmon
,
Ali Farhadi
European Conference on Computer Vision
2016
Corpus ID: 14925907
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In…
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Highly Cited
2015
Highly Cited
2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
,
X. Zhang
,
Shaoqing Ren
,
Jian Sun
IEEE International Conference on Computer Vision
2015
Corpus ID: 13740328
Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier…
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Highly Cited
2015
Highly Cited
2015
Tiny ImageNet Visual Recognition Challenge
Ya Le
,
Xuan S. Yang
2015
Corpus ID: 16664790
In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified…
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Highly Cited
2014
Highly Cited
2014
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
,
Jia Deng
,
+9 authors
Li Fei-Fei
International Journal of Computer Vision
2014
Corpus ID: 2930547
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds…
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Highly Cited
2012
Highly Cited
2012
ImageNet classification with deep convolutional neural networks
A. Krizhevsky
,
Ilya Sutskever
,
Geoffrey E. Hinton
Communications of the ACM
2012
Corpus ID: 195908774
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC…
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Highly Cited
2009
Highly Cited
2009
ImageNet: A large-scale hierarchical image database
Jia Deng
,
Wei Dong
,
R. Socher
,
Li-Jia Li
,
K. Li
,
Li Fei-Fei
IEEE Conference on Computer Vision and Pattern…
2009
Corpus ID: 57246310
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to…
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