ImageNet

Known as: ImageNet Competition, ImageNet Large Scale Visual Recognition Challenge 
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… (More)
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Topic mentions per year

Topic mentions per year

1990-2017
020040019902017

Papers overview

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Highly Cited
2016
Highly Cited
2016
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In… (More)
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Highly Cited
2016
Highly Cited
2016
Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when… (More)
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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… (More)
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Highly Cited
2015
Highly Cited
2015
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds… (More)
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Highly Cited
2014
Highly Cited
2014
Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations. This lack in… (More)
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Highly Cited
2013
Highly Cited
2013
  • 2013
The intended goal of the experiments was to create a deep, convolutional network that uses supervised learning to achieve better… (More)
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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… (More)
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Highly Cited
2012
Highly Cited
2012
ImageNet is a large-scale hierarchical database of object classes. We propose to automatically populate it with pixelwise… (More)
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Highly Cited
2011
Highly Cited
2011
Many computer vision approaches take for granted positive answers to questions such as “Are semantic categories visually… (More)
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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… (More)
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