Understand
Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks.
- With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales.
- Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain changes in appearance.
- Recent results on the ImageNet dataset show that given enough data, ConvNets can learn such invariances producing very discriminative features [1].
Built on
Handwritten digit recognition with a back-propagation network
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1990
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Yann LeCun, Fu Jie Huang, and Léon Bottou · 2004
Earlier work this paper cites.
Measuring invariances in deep networks
Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, and Andrew Y. Ng · 2009
Earlier work this paper cites.
Tiled convolutional neural networks
Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Jin hao Chia, Pang Wei Koh, and Andrew Y. Ng · 2010
Earlier work this paper cites.
Similar
Traffic sign recognition with multi-scale convolutional networks
Pierre Sermanet and Yann LeCun · 2011
Cited alongside, same era.
Transformation equivariant boltzmann machines
Jyri J. Kivinen and Christopher K. I. Williams · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Cited alongside, same era.
Learning invariant representations with local transformations
Kihyuk Sohn and Honglak Lee · 2012
Cited alongside, same era.
Semantic road segmentation via multi-scale ensembles of learned features
Jose M. Alvarez, Yann LeCun, Theo Gevers, and Antonio M. Lopez · 2012
Cited alongside, same era.
Then
Learning hierarchical features for scene labeling
Clément Farabet, Camille Couprie, Laurent Najman, and Yann LeCun · 2013
Later among the works it cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
Later among the works it cites.
Caffe: An open source convolutional architecture for fast feature embedding
Yangqing Jia · 2013
Later among the works it cites.
Regularization of neural networks using dropconnect
Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, and Rob Fergus · 2013
Later among the works it cites.
Beyond the bibliography
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…