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Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs.
Learning many related tasks at the same time with backpropagation
Caruana, R. (1995) · 1995
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P. (2004) · 2004
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009) · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. (2009) · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009) · 2009
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Deep learning of representations for unsupervised and transfer learning
Bengio, Y. (2011) · 2011
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Deep learners benefit more from out-of-distribution examples
Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., Côté, M., Erhan, D., Eustache, J., Glorot, X., Muller, X., Pannetier Lebeuf, S., Pascanu, R., Rifai, S., Savard, F., and Sicard, G. (2011) · 2011
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ICA with reconstruction cost for efficient overcomplete feature learning
Le, Q. V., Karpenko, A., Ngiam, J., and Ng, A. Y. (2011) · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012) · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012) · 2012
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2013) · 2013
Later among the works it cites.
Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2013) · 2013
Later among the works it cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2013) · 2013
Later among the works it cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014) · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2014) · 2014
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012) · 2012
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2013) · 2013
Cited alongside, same era.
Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2013a)
Cited in the paper.
Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2013b)
Cited in the paper.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014) · 2014
Closest in time.