Judging a Book By its Cover

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE , vol. 86, no. 11, pp. 2278–2324, 1998

1998

Earlier work this paper cites.

D. Pye, “Content-based methods for the management of digital music,” in Proc. 2000 IEEE Int. Conf. Acoustics, Speech, and Signal Process. , vol. 6. IEEE, 2000, pp. 2437–2440

2000

Earlier work this paper cites.

G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech Audio Process. , vol. 10, no. 5, pp. 293–302, 2002

2002

Earlier work this paper cites.

C. McKay and I. Fujinaga, “Automatic genre classification using large high-level musical feature sets.” in Int. Soc. of Music Inform. Retrieval , vol. 2004. Citeseer, 2004, pp. 525–530

2004

Earlier work this paper cites.

K. Chellapilla, S. Puri, and P. Simard, “High performance convolutional neural networks for document processing,” in 10th Int. Workshop Frontiers in Handwriting Recognition . Suvisoft, 2006

2006

Earlier work this paper cites.

R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Studying aesthetics in photographic images using a computational approach,” in 2006 European Conf. Comput. Vision . Springer, 2006, pp. 288–301

2006

Earlier work this paper cites.

A. Finn and N. Kushmerick, “Learning to classify documents according to genre,” J. Amer. Soc. for Inform. Sci. and Technology , vol. 57, no. 11, pp. 1506–1518, 2006

2006

Earlier work this paper cites.

R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” Assoc. Computing Mach. Computing Surveys , vol. 40, no. 2, p. 5, 2008

2008

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in 2012 IEEE Conf. Comput. Vision and Patern Recognition . IEEE, 2009, pp. 248–255

2009

Earlier work this paper cites.

J. Drucker and E. McVarish, Graphic Design History: A Critical Guide . Pearson Education, 2009

2009

Cited alongside, same era.

J. Zujovic, L. Gandy, S. Friedman, B. Pardo, and T. N. Pappas, “Classifying paintings by artistic genre: An analysis of features & classifiers,” in 2009 IEEE Int. Workshop Multimedia Signal Process. IEEE, 2009, pp. 1–5

2009

Cited alongside, same era.

P. Petrenz and B. Webber, “Stable classification of text genres,” Computational Linguistics , vol. 37, no. 2, pp. 385–393, 2011

2011

Cited alongside, same era.

D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conf. Comput. Vision and Pattern Recognition . IEEE, 2012, pp. 3642–3649

2012

Cited alongside, same era.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Inform. Process. Syst. , 2012, pp. 1097–1105

J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in Neural Inform. Process. Syst. , 2014, pp. 3320–3328

2014

Later among the works it cites.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Later among the works it cites.

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks , vol. 61, pp. 85–117, 2015

2015

Later among the works it cites.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Comp. Vision and Pattern Recognition , 2015, pp. 1–9

2015

Later among the works it cites.