Literature

Publications

1. Zeiler, M. D., Krishnan, D., Taylor, G. W. & Fergus, R. Deconvolutional Networks. at http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf
2. Bach, S. et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLoS One 10, e0130140 (2015).
3. Selvaraju, R. R. et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. at http://gradcam.cloudcv.org
4. Erhan Dumitru, Bengio Yoshua, Courville Aaron, V. P. Visualizing Higher-Layer Features of a Deep Network. at https://www.researchgate.net/profile/Aaron_Courville/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network/links/53ff82b00cf24c81027da530.pdf
5. Sundararajan, M., Taly, A. & Yan, Q. Axiomatic Attribution for Deep Networks. at https://arxiv.org/pdf/1703.01365.pdf
6. Smilkov, D. et al. Embedding Projector: Interactive Visualization and Interpretation of Embeddings. at https://arxiv.org/pdf/1611.05469.pdf
7. Van Der Maaten, L. & Hinton, G. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
8. Szegedy, C. et al. Intriguing properties of neural networks. at https://arxiv.org/pdf/1312.6199.pdf?not-changed
9. Goodfellow, I. J., Shlens, J. & Szegedy, C. EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES. at https://arxiv.org/pdf/1412.6572.pdf
10. Nguyen, A., Yosinski, J. & Clune, J. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. at http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf
11. Dosovitskiy, A. & Brox, T. Inverting Convolutional Networks with Convolutional Networks. at https://pdfs.semanticscholar.org/993c/55eef970c6a11ec367dbb1bf1f0c1d5d72a6.pdf
12. Mahendran, A. & Vedaldi, A. Understanding Deep Image Representations by Inverting Them. at http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf
13. Nguyen, A. et al. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. at http://papers.nips.cc/paper/6519-synthesizing-the-preferred-inputs-for-neurons-in-neural-networks-via-deep-generator-networks.pdf
14. Nguyen, A., Yosinski, J. & Clune, J. Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. at https://pdfs.semanticscholar.org/e184/6e3e95f5cec862e9b6f812e426908fcb46c7.pdf
15. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. & Lipson, H. Understanding Neural Networks Through Deep Visualization. (2015). at https://arxiv.org/pdf/1506.06579.pdf
16. Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. STRIVING FOR SIMPLICITY: THE ALL CONVOLUTIONAL NET. at <https://arxiv.org/pdf/1412.6806.pdf (http://arxiv.org/pdf/1412.6806.pdf)>
17. Simonyan, K., Vedaldi, A. & Zisserman, A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. at https://arxiv.org/pdf/1312.6034.pdf
18. Shi, S., Wang, Q., Xu, P. & Chu, X. Benchmarking State-of-the-Art Deep Learning Software Tools. at https://arxiv.org/pdf/1608.07249v7.pdf
19. Zeiler, M. D. & Fergus, R. Visualizing and Understanding Convolutional Networks. (2013). at http://arxiv.org/abs/1311.2901
20. Szegedy, C. et al. Intriguing properties of neural networks. (2013). at http://arxiv.org/abs/1312.6199
21. Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. (2013). at http://arxiv.org/abs/1311.2524
22. How neural networks build up their understanding of images: https://distill.pub/2017/feature-visualization/

Weblinks

• https://medium.com/@hint_fm/design-and-redesign-4ab77206cf9
• http://cs231n.stanford.edu/
• https://bcourses.berkeley.edu/courses/1453965
• http://lvdmaaten.github.io/tsne/
• http://scs.ryerson.ca/~aharley/vis/
• https://docs.google.com/presentation/d/1a-3bQwuc2Fjc1g8QeK6UuDzZOR1j1wvyWOxEyVWnxUc/edit#slide=id.g18174e0a77_0_1002
• https://github.com/jcjohnson/fast-neural-style
• https://icmlviz.github.io/reference/
• http://www.pinchofintelligence.com/simple-introduction-to-tensorboard-embedding-visualisation/
• https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
• https://hackernoon.com/visualizing-parts-of-convolutional-neural-networks-using-keras-and-cats-5cc01b214e59
• http://yosinski.com/deepvis
• http://scs.ryerson.ca/~aharley/vis/conv/
• https://icmlviz.github.io/
• https://distill.pub/2016/misread-tsne/
• https://github.com/Evolving-AI-Lab/synthesizing
• https://cs.stanford.edu/people/karpathy/convnetjs/
• https://pair-code.github.io/deeplearnjs/demos/imagenet/imagenet-demo.html
• https://github.com/PAIR-code/saliency
• https://github.com/InFoCusp/tf_cnnvis (visualizations for TensorFlow with static tensorboard integration)