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Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers Cover

Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

By: Ryan Henderson and  Rasmus Rothe  
Open Access
|Sep 2017

References

  1. Warren S M Walter P A logical calculus of the ideas immanent in nervous activity The bulletin of mathematical biophysics 1943 5 4 115 133 10.1007/BF02478259 ISSN 1522-9602
  2. Yann L Léon B Yoshua B Patrick H Gradient-based learning applied to document recognition Proceedings of the IEEE 1998 86 11 2278 2324 10.1109/5.726791
  3. Yann L Fu J H Leon B Learning methods for generic object recognition with invariance to pose and lighting Computer Vision and Pattern Recognition, CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, 2: II–104 2004 2004 IEEE 10.1109/CVPR.2004.1315150
  4. Alex K Ilya S Geoffrey E H Imagenet classification with deep convolutional neural networks Advances in neural information processing systems 2012 1097 1105
  5. Zhang G P Avoiding pitfalls in neural network research IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 2007 37 1 3 16 10.1109/TSMCC.2006.876059
  6. Chiyuan Z Samy B Moritz H Benjamin R Oriol V Understanding deep learning requires rethinking generalization arXiv preprint arXiv:1611.03530 2016 URL: https://arxiv.org/abs/1611.03530
  7. Eliezer Y Artificial intelligence as a positive and negative factor in global risk Global catastrophic risks 2008 1 303 184
  8. The Tesla Team A tragic loss 2016 URL: https://www.tesla.com/blog/tragic-loss. Accessed: 2017-5-12
  9. Karen S Andrew Z Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556 2014
  10. Matthew D Z Rob F Visualizing and Understanding Convolutional Networks 2014 Cham Springer International Publishing 978-3-319-10590-1 818 833 10.1007/978-3-319-10590-1
  11. Karen S Andrea V Andrew Z Deep inside convolutional networks: Visualising image classification models and saliency maps CoRR, abs/1312.6034 2013 URL: http://arxiv.org/abs/1312.6034
  12. Franois C K 2015 URL: https://github.com/fchollet/keras
  13. Martín A TensorFlow: Large-scale machine learning on heterogeneous systems 2015 URL: http://tensorflow.org/. Software available from: http://tensorow.org/
  14. Yann L Corinna C MNIST handwritten digit database 2010 URL: http://yann.lecun.com/exdb/mnist/
  15. Jason Y Jeff C Anh N Thomas F Understanding neural networks through deep visualization Deep Learning Workshop, International Conference on Machine Learning (ICML) 2015 URL: https://github.com/yosinski/deep-visualization-toolbox
  16. Raghavendra Kotikalapudi and contributors keras-vis 2017 https://github.com/raghakot/keras-vis
  17. Bolei Z Aditya K Agata L Aude O Antonio T Learning deep features for discriminative localization Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016 2921 2929 URL: http://cnnlocalization.csail.mit.edu/
  18. Ramprasaath R S Abhishek D Ramakrishna V Michael C Devi P Dhruv B Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization CoRR, abs/1610.02391 2016 URL: http://arxiv.org/abs/1610.02391
  19. Yi Li Haozhi Q Jifeng D Xiangyang J Yichen W Fully convolutional instance-aware semantic segmentation CoRR, abs/1611.07709 2016 URL: http://arxiv.org/abs/1611.07709
  20. Kaiming H Georgia G Piotr D Ross B G Mask R-CNN CoRR, abs/1703.06870 2017 URL: http://arxiv.org/abs/1703.06870
  21. U.S. Army US Army operating Renault FT tanks URL: https://en.wikipedia.org/wiki/Light_tank#/media/File:FT-17-argonne-1918.gif
DOI: https://doi.org/10.5334/jors.178 | Journal eISSN: 2049-9647
Language: English
Submitted on: May 16, 2017
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Accepted on: Jul 27, 2017
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Published on: Sep 11, 2017
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2017 Ryan Henderson, Rasmus Rothe, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.