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During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc.
Experiments on Learning by Back Propagation
D. Plaut et al · 1986
Earlier work this paper cites.
Handwritten Digit Recognition with a Back-Propagation Network
Y. LeCun, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel · 1990
Earlier work this paper cites.
Regularization Using Jittered Training Data
R. Reed, S. Oh, R. Marks, et al · 1992
Earlier work this paper cites.
Gradient-based Learning Applied to Document Recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
A. Torralba, R. Fergus, and W. Freeman · 2008
Earlier work this paper cites.
Extracting and Composing Robust Features with Denoising Autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol · 2008
Earlier work this paper cites.
ImageNet: A Large-scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
What is the Best Multi-Stage Architecture for Object Recognition?
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Large-Scale Machine Learning with Stochastic Gradient Descent
L. Bottou · 2010
Earlier work this paper cites.
Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
D. Ciresan, U. Meier, L. Gambardella, and J. Schmidhuber · 2010
Earlier work this paper cites.
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol · 2010
Earlier work this paper cites.
Reading Digits in Natural Images with Unsupervised Feature Learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Ng · 2011
Earlier work this paper cites.
Improving Neural Networks by Preventing Co-adaptation of Feature Detectors
G. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2012
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Cited alongside, same era.
Convolutional Neural Networks Applied to House Numbers Digit Classification
P. Sermanet, S. Chintala, and Y. LeCun · 2012
Cited alongside, same era.
Maxout Networks
I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
Cited alongside, same era.
Learning with Marginalized Corrupted Features
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Cited alongside, same era.
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Cited alongside, same era.
Discriminative Transfer Learning with Tree-based Priors
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Cited alongside, same era.
CNN Features off-the-shelf: an Astounding Baseline for Recognition
A. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Later among the works it cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
Later among the works it cites.
Training Convolutional Networks with Noisy Labels
S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus · 2014
Later among the works it cites.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Later among the works it cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
S. Ioffe and C. Szegedy · 2015
Later among the works it cites.
Dropout as Data Augmentation
K. Konda, X. Bouthillier, R. Memisevic, and P. Vincent · 2015
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Regularization of Neural Networks using DropConnect
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Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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B. Graham · 2014
Cited alongside, same era.
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G. Hinton, O. Vinyals, and J. Dean · 2014
Cited alongside, same era.
CAFFE: Convolutional Architecture for Fast Feature Embedding
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Deeply-Supervised Nets
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Recurrent Convolutional Neural Network for Object Recognition
M. Liang and X. Hu · 2015
Later among the works it cites.
ImageNet Large Scale Visual Recognition Challenge
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Later among the works it cites.
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Image Classification and Retrieval are ONE
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Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
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