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Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification.
A real-time algorithm for signal analysis with the help of the wavelet transform
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Backpropagation applied to handwritten zip code recognition
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Multiscale conditional random fields for image labeling
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The pyramid match kernel: Discriminative classification with sets of image features
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
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Robust higher order potentials for enforcing label consistency
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Associative hierarchical crfs for object class image segmentation
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Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
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Semantic contours from inverse detectors
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
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Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
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Fast image scanning with deep max-pooling convolutional neural networks
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Augmenting crfs with boltzmann machine shape priors for image labeling
A. Kae, K. Sohn, H. Lee, and E. Learned-Miller · 2013
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The pascal visual object classes challenge – a retrospective
M. Everingham, S. M. A. Eslami, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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Microsoft coco: Common objects in context
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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Convolutional feature masking for joint object and stuff segmentation
J. Dai, K. He, and J. Sun · 2015
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K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber · 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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An empirical exploration of recurrent network architectures
R. Jozefowicz, W. Zaremba, and I. Sutskever · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection
G. Papandreou, I. Kokkinos, and P.-A. Savalle · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 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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Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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The devil is in the decoder
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Genetic cnn
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
Cited alongside, same era.
Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
Cited alongside, same era.
Beyond skip connections: Top-down modulation for object detection
A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
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Maskconnect: Connectivity learning by gradient descent
K. Ahmed and L. Torresani · 2018
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In-place activated batchnorm for memory-optimized training of dnns
S. R. Bulò, L. Porzi, and P. Kontschieder · 2018
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Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2018
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Searching for efficient multi-scale architectures for dense image prediction
L.-C. Chen, M. D. Collins, Y. Zhu, G. Papandreou, B. Zoph, F. Schroff, H. Adam, and J. Shlens · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Dropblock: A regularization method for convolutional networks
G. Ghiasi, T.-Y. Lin, and Q. V. Le · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Multi-scale context intertwining for semantic segmentation
D. Lin, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2018
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2018
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Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
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Neural architecture optimization
R. Luo, F. Tian, T. Qin, and T.-Y. Liu · 2018
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Differentiable neural network architecture search
R. Shin, C. Packer, and D. Song · 2018
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Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le · 2018
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Understanding convolution for semantic segmentation
P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell · 2018
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Group normalization
Y. Wu and K. He · 2018
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Unified perceptual parsing for scene understanding
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
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Exfuse: Enhancing feature fusion for semantic segmentation
Z. Zhang, X. Zhang, C. Peng, D. Cheng, and J. Sun · 2018
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Practical block-wise neural network architecture generation
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu · 2018
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Dense relation network: Learning consistent and context-aware representation for semantic image segmentation
Y. Zhuang, F. Yang, L. Tao, C. Ma, Z. Zhang, Y. Li, H. Jia, X. Xie, and W. Gao · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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