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Deep learning methods have typically been trained on large datasets in which many training examples are available.
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The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories
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One shot learning of simple visual concepts
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Orb: an efficient alternative to sift or surf
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Measuring the objectness of image windows
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A learned feature descriptor for object recognition in rgb-d data
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Bing: Binarized normed gradients for objectness estimation at 300fps
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Unsupervised domain adaptation by backpropagation
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Exploring invariances in deep convolutional neural networks using synthetic images
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Cnn features off-the-shelf: an astounding baseline for recognition
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Bigbird: A large-scale 3d database of object instances
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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