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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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Papers overview

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Highly Cited
2019
Highly Cited
2019
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… 
Highly Cited
2018
Highly Cited
2018
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained… 
Highly Cited
2018
Highly Cited
2018
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations… 
Highly Cited
2017
Highly Cited
2017
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in… 
Highly Cited
2016
Highly Cited
2016
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In… 
Highly Cited
2015
Highly Cited
2015
Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier… 
Highly Cited
2015
Highly Cited
2015
In this work, we investigate the effect of convolutional network depth, receptive field size, dropout layers, rectified… 
Highly Cited
2014
Highly Cited
2014
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds… 
Highly Cited
2012
Highly Cited
2012
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC… 
Highly Cited
2009
Highly Cited
2009
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to…