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Deep networks have produced significant gains for various visual recognition problems, leading to high impact academic and commercial applications.
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Imagenet classification with deep convolutional neural networks
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W. J. Scheirer, A. de Rezende Rocha, A. Sapkota, and T. E. Boult · 2013
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Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C. D. Manning, and A. Ng · 2013
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Return of the devil in the details: Delving deep into convolutional nets
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Local novelty detection in multi-class recognition problems
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From captions to visual concepts and back
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Explaining and harnessing adversarial examples
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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ImageNet Large Scale Visual Recognition Challenge
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Incremental learning of ncm forests for large scale image classification
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Probability models for open set recognition
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Intriguing properties of neural networks
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Deepface: Closing the gap to human-level performance in face verification
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Predicting failures of vision systems
P. Zhang, J. Wang, A. Farhadi, M. Hebert, and D. Parikh · 2014
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Towards open world recognition
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