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We introduce an approach to training a given compact network.
Neural networks and principal component analysis: Learning from examples without local minima
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
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Imagenet classification with deep convolutional neural networks
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Predicting parameters in deep learning
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
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Fitnets: Hints for thin deep nets
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
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Distilling the knowledge in a neural network
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Flattened convolutional neural networks for feedforward acceleration
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Sparse convolutional neural networks
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Fully convolutional networks for semantic segmentation
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Faster r-cnn: Towards real-time object detection with region proposal networks
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U-net: Convolutional networks for biomedical image segmentation
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Imagenet large scale visual recognition challenge
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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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Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
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The cityscapes dataset for semantic urban scene understanding
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Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Perforatedcnns: Acceleration through elimination of redundant convolutions
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
S. Zagoruyko and N. Komodakis · 2017
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
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On the optimization of deep networks: Implicit acceleration by overparameterization
S. Arora, N. Cohen, and E. Hazan · 2018
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“learning-compression” algorithms for neural net pruning
A. M. Carreira-Perpinan and Y. Idelbayev · 2018
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Implicit bias of gradient descent on linear convolutional networks
S. Gunasekar, J. D. Lee, D. Soudry, and N. Srebro · 2018
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Yolo-lite: A real-time object detection algorithm optimized for non-gpu computers
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M. Figurnov, A. Ibraimova, D. P. Vetrov, and P. Kohli · 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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Deep learning without poor local minima
K. Kawaguchi · 2016
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Yolo9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving
B. Wu, F. Iandola, P. H. Jin, and K. Keutzer · 2016
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R. Huang, J. Pedoeem, and C. Chen · 2018
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Generalization in deep learning
K. Kawaguchi, L. P. Kaelbling, and Y. Bengio · 2018
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Deep linear networks with arbitrary loss: All local minima are global
T. Laurent and J. von Brecht · 2018
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Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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Learning deep representations with probabilistic knowledge transfer
N. Passalis and A. Tefas · 2018
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Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 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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Critical points of linear neural networks: Analytical forms and landscape properties
Y. Zhou and Y. Liang · 2018
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Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks
X. Ding, Y. Guo, G. Ding, and J. Han · 2019
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Snip: Single-shot network pruning based on connection sensitivity
N. Lee, T. Ajanthan, and P. H. Torr · 2019
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The impact of neural network overparameterization on gradient confusion and stochastic gradient descent
K. A. Sankararaman, S. De, Z. Xu, W. R. Huang, and T. Goldstein · 2019
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Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
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Contrastive representation distillation
Y. Tian, D. Krishnan, and P. Isola · 2020
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