Fetching the paper…
Reading the bibliography…
When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain.
Optimal brain damage
Y. LeCun, J. S. Denker, and S. A. Solla · 1990
Earlier work this paper cites.
Second order derivatives for network pruning: optimal brain surgeon
B. Hassibi and D. G. Stork · 1992
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
Earlier work this paper cites.
Bag-of-visual-words and spatial extensions for land-use classification
Y. Yang and S. Newsam · 2010
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Earlier work this paper cites.
Bayesian optimization in high dimensions via random embeddings
Z. Wang, M. Zoghi, F. Hutter, D. Matheson, and N. de Freitas · 2013
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
Earlier work this paper cites.
Bayesian optimization with inequality constraints
J. R. Gardner, M. J. Kusner, Z. Xu, K. Q. Weinberger, and J. P. Cunningham · 2014
Earlier work this paper cites.
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 · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
An exploration of parameter redundancy in deep networks with circulant projections
Y. Cheng, F. X. Yu, R. S. Feris, S. Kumar, A. Choudhary, and S.-F. Chang · 2015
Cited alongside, same era.
BinaryConnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
PoseNet: A convolutional network for real-time 6-DOF camera relocalization
A. Kendall, M. Grimes, and R. Cipolla · 2015
Cited alongside, same era.
FitNets: hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and ¡0.5MB model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Later among the works it cites.
Pairwise decomposition of image sequences for active multi-view recognition
E. Johns, S. Leutenegger, and A. J. Davison · 2016
Later among the works it cites.
Fast ConvNets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
Later among the works it cites.
SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Later among the works it cites.
ACDC: A structured efficient linear layer
M. Moczulski, M. Denil, J. Appleyard, and N. de Freitas · 2016
Later among the works it cites.
Volumetric and multi-view CNNs for object classification on 3D data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Data-free parameter pruning for deep neural networks
S. Srinivas and R. V. Babu · 2015
Cited alongside, same era.
Learning spatiotemporal features with 3D convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
Cited alongside, same era.
Deep fried convnets
Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang · 2015
Cited alongside, same era.
NetVLAD: CNN architecture for weakly supervised place recognition
R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic · 2016
Cited alongside, same era.
Dynamic network surgery for efficient DNNs
Y. Guo, A. Yao, and Y. Chen · 2016
Cited alongside, same era.
EIE: Efficient inference engine on compressed deep neural network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally
Cited in the paper.
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
Later among the works it cites.
XNOR-Net: ImageNet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Later among the works it cites.
You only look once: unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Later among the works it cites.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Later among the works it cites.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Later among the works it cites.
Less is more: towards compact CNNs
H. Zhou, J. M. Alvarez, and F. Porikli · 2016
Later among the works it cites.