Very Deep Convolutional Networks for Large-Scale Image Recognition

@article{Simonyan2014VeryDC,
  title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
  author={Karen Simonyan and Andrew Zisserman},
  journal={CoRR},
  year={2014},
  volume={abs/1409.1556}
}
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

S

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh
  • Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet large scal e visual recognition challenge. CoRR, abs/1409.0575
  • 2014
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

a nd Y

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus
  • LeCun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Netw orks. InProc. ICLR
  • 2014
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

and L

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li
  • Fei-Fei . Imagenet: A large-scale hierarchical image database. InProc. CVPR
  • 2009
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

R

Y. LeCun, B. Boser, J. S. Denker, D. Henderson
  • E. Howa rd, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541–551
  • 1989
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
  • 2014
VIEW 1 EXCERPT

D

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed
  • Angue lov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, abs/1409.4842
  • 2014
VIEW 1 EXCERPT

ImageNet Large Scale Visual Recognition Challenge

  • International Journal of Computer Vision
  • 2014