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Recent enhancements of deep convolutional neural networks (ConvNets) empowered by enormous amounts of labeled data have closed the gap with human performance for many object recognition tasks.
Mean shift: A robust approach toward feature space analysis
D. Comaniciu and P. Meer · 2002
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Grabcut: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
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Scene completion using millions of photographs
J. Hays and A. A. Efros · 2007
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Patchmatch: A randomized correspondence algorithm for structural image editing
C. Barnes, E. Shechtman, A. Finkelstein, and D. Goldman · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Y. Ng, and H. Lee · 2011
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Multi-column deep neural networks for image classification
D. Ciresan, U. Meier, and J. Schmidhuber · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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A fast spatial patch blending algorithm for artefact reduction in pattern-based image inpainting
M. Daisy, D. Tschumperlé, and O. Lézoray · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2013
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M. Lin, Q. Chen, and S. Yan · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Synthetic data and artificial neural networks for natural scene text recognition
Cnn features off-the-shelf: an astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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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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M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Understanding image representations by measuring their equivariance and equivalence
K. Lenc and A. Vedaldi · 2014
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2014
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Exploring invariances in deep convolutional neural networks using synthetic images
X. Peng, B. Sun, K. Ali, and K. Saenko · 2014
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Dual aperture photography: Image and depth from a mobile camera
M. Martinello, A. Wajs, S. Quan, H. Lee, C. Lim, T. Woo, W. Lee, S.-S. Kim, and D. Lee
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Light field photography with a hand-held plenoptic camera
R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan
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M. D. Zeiler and R. Fergus · 2014
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Deep image: Scaling up image recognition
R. Wu, S. Yan, Y. Shan, Q. Dang, and G. Sun · 2015
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