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How convolutional neural networks see the world --- A survey of convolutional neural network visualization methods
1. | George Mason University, 4400 University Dr, Fairfax, VA 22030, USA |
2. | Clarkson University, 8 Clarkson Ave, Potsdam, NY 13699, USA |
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs' outstanding capability to learn the input features with deep layers of neuron structures and iterative training process. However, these learned features are hard to identify and interpret from a human vision perspective, causing a lack of understanding of the CNNs' internal working mechanism. To improve the CNN interpretability, the CNN visualization is well utilized as a qualitative analysis method, which translates the internal features into visually perceptible patterns. And many CNN visualization works have been proposed in the literature to interpret the CNN in perspectives of network structure, operation, and semantic concept.
In this paper, we expect to provide a comprehensive survey of several representative CNN visualization methods, including Activation Maximization, Network Inversion, Deconvolutional Neural Networks (DeconvNet), and Network Dissection based visualization. These methods are presented in terms of motivations, algorithms, and experiment results. Based on these visualization methods, we also discuss their practical applications to demonstrate the significance of the CNN interpretability in areas of network design, optimization, security enhancement, etc.
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show all references
References:
[1] |
P. Agrawal, R. Girshick and J. Malik, Analyzing the performance of multilayer neural networks for object recognition, in Proceedings of the European Conference on Computer Vision, 2014, 329-344.
doi: 10.1007/978-3-319-10584-0_22. |
[2] |
M. Arjovsky, S. Chintala and L. Bottou, Wasserstein gan, arXiv preprint, arXiv: 1701.07875. Google Scholar |
[3] |
D. Bau, B. Zhou, A. Khosla, A. Oliva and A. Torralba, Network dissection: Quantifying interpretability of deep visual representations, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 3319-3327.
doi: 10.1109/CVPR.2017.354. |
[4] |
D. C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella and J. Schmidhuber, Flexible, High performance convolutional neural networks for image classification, in Proceedings of the International Joint Conference on Artificial Intelligence, vol. 22, 2011, p1237. Google Scholar |
[5] |
R. Collobert, K. Kavukcuoglu and C. Farabet, Torch7: A matlab-like environment for machine learning, in Workshop on BigLearn, NIPS, 2011. Google Scholar |
[6] |
G. Csurka, C. Dance, L. Fan, J. Willamowski and C. Bray, Visual categorization with bags of keypoints, in Workshop on statistical learning in computer vision, ECCV, vol. 1, 2004, 1-2. Google Scholar |
[7] |
N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, 886-893.
doi: 10.1109/CVPR.2005.177. |
[8] |
E. d'Angelo, A. Alahi and P. Vandergheynst, Beyond bits: Reconstructing images from local binary descriptors, in Proceedings of the IEEE Conference on Pattern Recognition, 2012, 935-938. Google Scholar |
[9] |
E. L. Denton, S. Chintala, R. Fergus et al., Deep generative image models using a Laplacian pyramid of adversarial networks, in Proceedings of the Advances in Neural Information Processing Systems, 2015, 1486-1494. Google Scholar |
[10] |
A. Dosovitskiy and T. Brox, Generating images with perceptual similarity metrics based on deep networks, in Proceedings of the Advances in Neural Information Processing Systems, 2016, 658-666. Google Scholar |
[11] |
A. Dosovitskiy and T. Brox, Inverting visual representations with convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 4829-4837.
doi: 10.1109/CVPR.2016.522. |
[12] |
A. Dosovitskiy, J. Tobias Springenberg and T. Brox, Learning to generate chairs with convolutional neural networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1538-1546.
doi: 10.1109/CVPR.2015.7298761. |
[13] |
J. Duchi, E. Hazan and Y. Singer,
Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, 12 (2011), 2121-2159.
|
[14] |
D. Erhan, Y. Bengio, A. Courville and P. Vincent, Visualizing higher-layer features of a deep network, Technical report, University of Montreal, (2009), p3. Google Scholar |
[15] |
P. F. Felzenszwalb, R. B. Girshick, D. McAllester and D. Ramanan,
Object detection with discriminatively trained part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (2010), 1627-1645.
doi: 10.1109/TPAMI.2009.167. |
[16] |
R. Fong and A. Vedaldi, Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks, arXiv preprint, arXiv: 1801.03454. Google Scholar |
[17] |
L. A. Gatys, A. S. Ecker and M. Bethge, A neural algorithm of artistic style, Journal of Vision, 16 (2016), p326, arXiv: 1508.06576.
doi: 10.1167/16.12.326. |
[18] |
L. A. Gatys, A. S. Ecker and M. Bethge, Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks, arXiv preprint, arXiv: 1505.07376, 12. Google Scholar |
[19] |
R. B. Girshick, P. F. Felzenszwalb and D. McAllester, Discriminatively trained deformable part models, release 5, http://people.cs.uchicago.edu/~rbg/latent-release5/. Google Scholar |
[20] |
R. Girshick, J. Donahue, T. Darrell and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, 580-587.
doi: 10.1109/CVPR.2014.81. |
[21] |
X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in Proceedings of the International Conference on Artificial Intelligence and Statistics, 2010, 249-256. Google Scholar |
[22] |
Y. Gong, L. Wang, R. Guo and S. Lazebnik, Multi-scale orderless pooling of deep convolutional activation features, in Proceedings of the European Conference on Computer Vision, 2014, 392-407.
doi: 10.1007/978-3-319-10584-0_26. |
[23] |
A. Gonzalez-Garcia, D. Modolo and V. Ferrari,
Do semantic parts emerge in convolutional neural networks?, International Journal of Computer Vision, 126 (2018), 476-494.
doi: 10.1007/s11263-017-1048-0. |
[24] |
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative adversarial nets, in Proceedings of the Advances in Neural Information Processing Systems, 2014, 2672-2680. Google Scholar |
[25] |
A. Gordo, J. Almazán, J. Revaud and D. Larlus, Deep image retrieval: Learning global representations for image search, in Proceedings of the European Conference on Computer Vision, Springer, 2016, 241-257.
doi: 10.1007/978-3-319-46466-4_15. |
[26] |
S. Han, H. Mao and W. J. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, arXiv preprint, arXiv: 1510.00149. Google Scholar |
[27] |
K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2016, 770-778.
doi: 10.1109/CVPR.2016.90. |
[28] |
G. E. Hinton, S. Osindero and Y.-W. Teh,
A fast learning algorithm for deep belief nets, Neural Computation, 18 (2006), 1527-1554.
doi: 10.1162/neco.2006.18.7.1527. |
[29] |
D. H. Hubel and T. N. Wiesel, Receptive fields and functional architecture of monkey striate cortex, The Journal of Physiology, 195 (1968), 215-243, URL http://dx.doi.org/10.1113/jphysiol.1968.sp008455.
doi: 10.1113/jphysiol.1968.sp008455. |
[30] |
D. H. Hubel and T. N. Wiesel,
Receptive fields of single neurones in the cat's striate cortex, The Journal of Physiology, 148 (1959), 574-591.
doi: 10.1113/jphysiol.1959.sp006308. |
[31] |
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proceedings of the International Conference on Machine Learning, 2015, 448-456. Google Scholar |
[32] |
S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proceedings of the International Conference on Machine Learning, 2015, 448-456. Google Scholar |
[33] |
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama and T. Darrell, Caffe: Convolutional architecture for fast feature embedding, in Proceedings of the International Conference on Multimedia, 2014, 675-678.
doi: 10.1145/2647868.2654889. |
[34] |
G.-S. Kalanit and M. Rafael, The human visual cortex, Annual Review of Neuroscience, 27 (2004), 649-677. Google Scholar |
[35] |
K. N. Kay, T. Naselaris, R. J. Prenger and J. L. Gallant,
Identifying natural images from human brain activity, Nature, 452 (2008), p352.
doi: 10.1038/nature06713. |
[36] |
A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Proceedings of the Advances in Neural Information Processing Systems, 2012, 1097-1150.
doi: 10.1145/3065386. |
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Method | Interpretation Perspective | Focused Layer | Applied Network | Representative Study |
Activation Maximization | Individual Neuron with visualized pattern | CLs FLs |
Auto-Encoder, DBN, AlexNet | [26] |
Deconvolutional Neural Networks | Neuron activation in input image | CLs | AlexNet | [55] |
Network Inversion | One layer | CLs FLs |
HOG, SIFT, LBD, Bag of words, CaffeNet | [29][64] |
Network Dissection | Individual Neuron with semantic concept | CLs | AlexNet, VGG, GoogLeNet, ResNet | [32][70] |
Method | Interpretation Perspective | Focused Layer | Applied Network | Representative Study |
Activation Maximization | Individual Neuron with visualized pattern | CLs FLs |
Auto-Encoder, DBN, AlexNet | [26] |
Deconvolutional Neural Networks | Neuron activation in input image | CLs | AlexNet | [55] |
Network Inversion | One layer | CLs FLs |
HOG, SIFT, LBD, Bag of words, CaffeNet | [29][64] |
Network Dissection | Individual Neuron with semantic concept | CLs | AlexNet, VGG, GoogLeNet, ResNet | [32][70] |
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