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This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
The dynamic representation of scenes
R. A. Rensink · 2000
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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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Learning to combine foveal glimpses with a third-order boltzmann machine
H. Larochelle and G. E. Hinton · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, et al · 2014
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Multiple object recognition with visual attention
J. Ba, V. Mnih, and K. Kavukcuoglu · 2015
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Tobias Springenberg, and T. Brox · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2015
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Deepfix: A fully convolutional neural network for predicting human eye fixations
S. S. Kruthiventi, K. Ayush, and R. V. Babu · 2015
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A convolutional neural network cascade for face detection
H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo · 2015
Adaptive computation time for recurrent neural networks
A. Graves · 2016
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2016
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
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Dynamic capacity networks
A. Almahairi, N. Ballas, T. Cooijmans, Y. Zheng, H. Larochelle, and A. Courville · 2016
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Conditional computation in neural networks for faster models
E. Bengio, P.-L. Bacon, J. Pineau, and D. Precup · 2016
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What do different evaluation metrics tell us about saliency models?
Z. Bylinskii, T. Judd, A. Oliva, A. Torralba, and F. Durand · 2016
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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My notes on adaptive computation time for recurrent neural networks
H. Larochelle · 2016
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Videolstm convolves, attends and flows for action recognition
Z. Li, E. Gavves, M. Jain, and C. G. Snoek · 2016
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Q. Liao and T. Poggio · 2016
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Action recognition using visual attention
S. Sharma, R. Kiros, and R. Salakhutdinov · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
S. Teerapittayanon, B. McDanel, and H. Kung · 2016
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Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers
F. Yang, W. Choi, and Y. Lin · 2016
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Highway and residual networks learn unrolled iterative estimation
K. Greff, R. Srivastava, and J. Schmidhuber · 2017
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