The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Convolutional Neural Network Committees for Handwritten Character Classification
Abstract
In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.
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Gradient-based learning applied to document recognition
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What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
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The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
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Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057
Self-citationDeep visual tracking: Review and experimental comparison
Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517
Minerva: enabling low-power, highly-accurate deep neural network accelerators
Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001165 · Cited by 446
Metadata
- Authors
- Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J.
- Venue
- 2011 International Conference on Document Analysis and Recognition
- Publisher
- IEEE
- Published
- 2011-09
- Pages
- 1135-1139
- ISSN
- 1520-5363 (ISSN-L)
- OpenAlex
- W2033154814
- Cited by
- 536
- References
- 5
- FWCI
- 13.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011). Convolutional Neural Network Committees for Handwritten Character Classification. 2011 International Conference on Document Analysis and Recognition. https://doi.org/10.1109/icdar.2011.229
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