Deep inside convolutional networks: Visualising image classification models and saliency maps

2nd International Conference on Learning Representations, ICLR 2014 - Workshop Track Proceedings

Volumn , Issue , 2014, Pages

  Simonyan, Karen ^a   Vedaldi, Andrea ^a   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; IMAGE SEGMENTATION; VISUALIZATION;

CLASSIFICATION MODELS; CONVNET; CONVOLUTIONAL NETWORKS; GRADIENT BASED; INPUT IMAGE; OBJECT SEGMENTATION; SALIENCY MAP;

IMAGE CLASSIFICATION;

EID: 85083953896     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller. How to explain individual classification decisions. JMLR, 11:1803–1831, 2010.
2. A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge (ILSVRC), 2010. URL http://www.image-net.org/challenges/LSVRC/2010/.
3. Y. Boykov and M. P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In Proc. ICCV, volume 2, pages 105–112, 2001.
4. D. C. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In Proc. CVPR, pages 3642–3649, 2012.
5. D. Erhan, Y. Bengio, A. Courville, and P. Vincent. Visualizing higher-layer features of a deep network. Technical Report 1341, University of Montreal, Jun 2009.
6. P. Felzenszwalb, D. Mcallester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Proc. CVPR, 2008.
7. G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527–1554, 2006.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106–1114, 2012.
9. Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, and A. Ng. Building high-level features using large scale unsupervised learning. In Proc. ICML, 2012.
10. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
11. F. Perronnin, J. Sánchez, and T. Mensink. Improving the Fisher kernel for large-scale image classification. In Proc. ECCV, 2010.
12. K. Simonyan, A. Vedaldi, and A. Zisserman. Deep Fisher networks and class saliency maps for object classification and localisation. In ILSVRC workshop, 2013. URL http://image-net.org/challenges/LSVRC/2013/slides/ILSVRC_az.pdf.
13. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901v3, 2013.