A taxonomy of deep convolutional neural nets for computer vision

Frontiers Robotics AI

Volumn 2, Issue JAN, 2016, Pages

  Srinivas, Suraj ^a   Sarvadevabhatla, Ravi Kiran ^a   Mopuri, Konda Reddy ^a   Prabhu, Nikita ^a   Kruthiventi, Srinivas S S ^a   Babu, R Venkatesh ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Convolutional neural networks; Deep learning; Object classification; Recurrent neural networks; Supervised learning

Indexed keywords

EID: 85034742569     PISSN: None     EISSN: 22969144     Source Type: Journal    
DOI: 10.3389/frobt.2015.00036     Document Type: Article

References (117)

1. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., et al. (2015). VQA: Visual Question Answering. arXiv preprint arXiv:1505.00468
2. Ba, J., and Caruana, R. (2014). "Do deep nets really need to be deep?," in Advances in Neural Information Processing Systems 27, eds Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger (Curran Associates, Inc.), 2654-2662
3. Babenko, A., Slesarev, A., Chigorin, A., and Lempitsky, V. (2014). "Neural codes for image retrieval," in Computer Vision-ECCV 2014 (Springer), 584-599
4. Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473
5. Baker, S., and Matthews, I. (2004). Lucas-kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56, 221-255. doi: 10.1023/B:VISI.0000011205.11775.fd
6. Bengio, Y. (2009). Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1-127. doi:10.1561/2200000006
7. Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2013). Deep Generative Stochastic Networks Trainable by Backprop. arXiv preprint arXiv:1306.1091
8. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ: Springer-Verlag New York, Inc
9. Bromley, J., Bentz, J. W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., et al. (1993). Signature verification using a siamese time delay neural network. Int. J. Pattern Recogn. Artif. Intell. 7, 669-688. doi:10.1142/S0218001493000339
10. Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). "Return of the devil in the details: delving deep into convolutional nets," in Proceedings of the British Machine Vision Conference (Nottingham: BMVA Press)
11. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv preprint arXiv:1412.7062
12. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., et al. (2014). Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078
13. Chopra, S., Hadsell, R., and LeCun, Y. (2005). "Learning a Similarity metric discriminatively, with application to face verification," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, Vol. 1 (IEEE), 539-546
14. Collins, M. D., and Kohli, P. (2014). Memory Bounded Deep Convolutional Networks. arXiv preprint arXiv:1412.1442
15. Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013). Indoor Semantic Segmentation Using Depth Information. CoRR, abs/1301.3572
16. Dalal, N., and Triggs, B. (2005). "Histograms of oriented gradients for human detection," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, Vol. 1 (IEEE), 886-893
17. Denil, M., Shakibi, B., Dinh, L., Ranzato, M. A., and de Freitas, N. (2013). "Predicting parameters in deep learning," in Advances in Neural Information Processing Systems 26, eds C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger (Curran Associates, Inc.), 2148-2156
18. Devries, T., Biswaranjan, K., and Taylor, G. W. (2014). "Multi-task learning of facial landmarks and expression," in Canadian Conference on Computer and Robot Vision (CRV), 2014, 98-103
19. Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., et al. (2014). Long-Term Recurrent Convolutional Networks for Visual Recognition and Description. arXiv preprint arXiv:1411.4389
20. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., et al. (2013). Decaf: A Deep Convolutional Activation Feature for Generic Visual Recognition. arXiv preprint arXiv:1310.1531
21. Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121-2159
22. Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013a). Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915-1929. doi:10.1109/TPAMI.2012.231
23. Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013b). Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915-1929. doi:10.1109/TPAMI.2012.231
24. Fukushima, K. (1980). Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193-202. doi:10.1007/BF00344251
25. Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., and Xu, W. (2015). Are you talking to a machine? Dataset and methods for multilingual image question answering. arXiv preprint arXiv:1505.05612
26. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (IEEE), 580-587
27. Girshick, R. B. (2015). Fast R-CNN. CoRR, abs/1504.08083
28. Gkioxari, G., and Malik, J. (2015). "Finding action tubes," in CVPR
29. Gong, Y., Wang, L., Guo, R., and Lazebnik, S. (2014a). "Multi-scale orderless pooling of deep convolutional activation features," in Computer Vision-ECCV 2014 (Springer International Publishing), 392-407
30. Gong, Y., Wang, L., Guo, R., and Lazebnik, S. (2014b). "Multi-scale orderless pooling of deep convolutional activation features," in Computer Vision-ECCV 2014 (Springer International Publishing), 392-407
31. Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014a). Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572
32. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014b). "Generative adversarial nets," in Advances in Neural Information Processing Systems 27, eds Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger (Curran Associates, Inc.), 2672-2680
33. Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013). Maxout Networks. arXiv preprint arXiv:1302.4389
34. Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31, 855-868. doi:10.1109/TPAMI.2008.137
35. Graves, A., Mohamed, A.-R., and Hinton, G. (2013). "Speech recognition with deep recurrent neural networks," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013 (IEEE), 6645-6649
36. Gupta, S., Girshick, R., Arbelaez, P., and Malik, J. (2014). Learning Rich Features from RGB-D Images for Object Detection and Segmentation
37. Hariharan, B., Arbeláez, P., Girshick, R., and Malik, J. (2014). Hypercolumns for Object Segmentation and Fine-Grained Localization. arXiv preprint arXiv:1411.5752
38. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. arXiv preprint arXiv:1502.01852
39. Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531
40. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771-1800. doi:10.1162/089976602760128018
41. Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527-1554. doi:10.1162/neco.2006.18.7.1527
42. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. (2012). Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. arXiv preprint arXiv:1207.0580
43. Hochreiter, S., and Schmidhuber, J. (1997). Long short-term memory. Neural Comput. 9, 1735-1780. doi:10.1162/neco.1997.9.8.1735
44. Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251-257. doi:10.1016/0893-6080(91)90009-T
45. Hubel, D. H., and Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160, 106. doi:10.1113/jphysiol.1962.sp006837
46. Jaderberg, M., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014a). Deep Structured Output Learning for Unconstrained Text Recognition. CoRR, abs/1412.5903
47. Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014b). Speeding up Convolutional Neural Networks with Low Rank Expansions. arXiv preprint arXiv:1405.3866
48. Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., and Schmid, C. (2011). Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell
49. Jhuang, H., Serre, T., Wolf, L., and Poggio, T. (2007). "A biologically inspired system for action recognition," in IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, 1-8
50. Ji, S., Xu, W., Yang, M., and Yu, K. (2010). 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221-231. doi:10.1109/TPAMI.2012.59
51. Karpathy, A., and Fei-Fei, L. (2014). Deep Visual-Semantic Alignments for Generating Image Descriptions. arXiv preprint arXiv:1412.2306
52. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014). "Large-scale video classification with convolutional neural networks," in Computer Vision and Pattern Recognition (CVPR) (Columbus, OH: IEEE), 1725-1732
53. Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980
54. Kingma, D. P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv preprint arXiv:1312.6114
55. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems 25, eds F. Pereira, C. Burges, L. Bottou, and K. Weinberger (Curran Associates, Inc.), 1097-1105
56. Kruthiventi, S. S., Ayush, K., and Babu, R. V. (2015). Deepfix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations. arXiv preprint arXiv:1510.02927
57. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. (2011). "HMDB: a large video database for human motion recognition," in IEEE International Conference on Computer Vision (ICCV), 2011 (IEEE), 2556-2563
58. Kulkarni, T. D., Whitney, W., Kohli, P., and Tenenbaum, J. B. (2015). Deep Convolutional Inverse Graphics Network. arXiv preprint arXiv:1503.03167
59. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521, 436-444. doi:10.1038/nature14539
60. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278-2324. doi:10.1109/5.726791
61. Li, S., Liu, Z.-Q., and Chan, A. (2015a). Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. Int. J. Comput. Vis. 113, 19-36. doi:10.1007/s11263-014-0767-8
62. Li, X., Zhao, L., Wei, L., Yang, M., Wu, F., Zhuang, Y., et al. (2015b). Deepsaliency: Multi-Task Deep Neural Network Model for Salient Object Detection. arXiv preprint arXiv:1510.05484
63. Liu, N., Han, J., Zhang, D., Wen, S., and Liu, T. (2015). Predicting Eye Fixations Using Convolutional Neural Networks
64. Long, J., Shelhamer, E., and Darrell, T. (2015). "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440
65. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91-110. doi:10.1023/B:VISI.0000029664.99615.94
66. Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013). "Rectifier nonlinearities improve neural network acoustic models," in Proc. ICML, Vol. 30
67. Malinowski, M., Rohrbach, M., and Fritz, M. (2015). Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images. arXiv preprint arXiv:1505.01121
68. Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. (2015). Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images. arXiv preprint arXiv:1504.06692
69. Mao, J., Xu, W., Yang, Y., Wang, J., and Yuille, A. (2014). Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). arXiv preprint arXiv:1412.6632
70. Mopuri, K. R., and Babu, R. V. (2015). "Object level deep feature pooling for compact image representation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 62-70
71. Nair, V., and Hinton, G. E. (2010). "Rectified linear units improve restricted Boltzmann machines," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807-814
72. Nesterov, Y. (1983). "A method of solving a convex programming problem with convergence rate o (1/k2)," in Soviet Mathematics Doklady, Vol. 27, 372-376
73. Nguyen, A., Yosinski, J., and Clune, J. (2014). Deep neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. arXiv preprint arXiv:1412.1897
74. Pan, Y., Mei, T., Yao, T., Li, H., and Rui, Y. (2015). Jointly Modeling Embedding and Translation to Bridge Video and Language. arXiv preprint arXiv:1505.01861
75. Polyak, B. T. (1964). Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4, 1-17. doi:10.1016/0041-5553(64)90137-5
76. Razavian, A. S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014). "CNN features off-the-shelf: an astounding baseline for recognition," in IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 (IEEE), 512-519
77. Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., and Bengio, Y. (2014). Fitnets: Hints for Thin Deep Nets. arXiv preprint arXiv:1412.6550
78. Rowley, H. A., Baluja, S., and Kanade, T. (1998). Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23-38. doi:10.1109/34.655647
79. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988). Learning representations by back-propagating errors. Cogn. Model. 5, 3
80. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015). Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211-252. doi:10.1007/s11263-015-0816-y
81. Schmidhuber, J. (2015). Deep learning in neural networks: an overview. Neural Netw. 61, 85-117. doi:10.1016/j.neunet.2014.09.003
82. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013a). Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks. arXiv preprint arXiv:1312.6229
83. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013b). Overfeat: Integrated Recognition, Localization and Detection using Convolutional Networks. CoRR, abs/1312.6229
84. Sermanet, P., Kavukcuoglu, K., Chintala, S., and LeCun, Y. (2013c). "Pedestrian detection with unsupervised multi-stage feature learning," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (IEEE), 3626-3633
85. Simonyan, K., and Zisserman, A. (2014a). "Two-stream convolutional networks for action recognition in videos," in Advances in Neural Information Processing Systems 27, eds Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger 568-576
86. Simonyan, K., and Zisserman, A. (2014b). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556
87. Snoek, J., Larochelle, H., and Adams, R. P. (2012). "Practical bayesian optimization of machine learning algorithms," in Advances in Neural Information Processing Systems, 25, eds F. Pereira, C. Burges, L. Bottou, and K. Weinberger (Curran Associates, Inc.), 2951-2959
88. Socher, R., Huval, B., Bath, B., Manning, C. D., and Ng, A. Y. (2012). "Convolutional-recursive deep learning for 3d object classification," in Advances in Neural Information Processing Systems 25, eds F. Pereira, C. Burges, L. Bottou, and K. Weinberger, 656-664
89. Srinivas, S., and Babu, R. V. (2015). "Data-free parameter pruning for deep neural networks," in Proceedings of the British Machine Vision Conference (BMVC), eds M. W. J. Xianghua Xie and G. K. L. Tam (BMVA Press), 31.1-31.12
90. Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013). "On the importance of initialization and momentum in deep learning," in Proceedings of the 30th International Conference on Machine Learning (ICML-13). 1139-1147
91. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2014). Going Deeper with Convolutions. arXiv preprint arXiv:1409.4842
92. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., et al. (2013). Intriguing Properties of Neural Networks. arXiv preprint arXiv:1312.6199
93. Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014). "Deepface: closing the gap to human-level performance in face verification," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (IEEE), 1701-1708
94. Uijlings, J., van de Sande, K., Gevers, T., and Smeulders, A. (2013). Selective search for object recognition. Int. J. of Comput. Vision doi:10.1007/s11263-013-0620-5
95. Vaillant, R., Monrocq, C., and Le Cun, Y. (1994). Original approach for the localisation of objects in images. IEE Proc. Vision Image Signal Process. 141, 245-250. doi:10.1049/ip-vis:19941301
96. Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., and Saenko, K. (2015). Sequence to Sequence-Video to Text. arXiv preprint arXiv:1505.00487
97. Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., and Saenko, K. (2014). Translating Videos to Natural Language Using Deep Recurrent Neural Networks. arXiv preprint arXiv:1412.4729
98. Vig, E., Dorr, M., and Cox, D. (2014). "Large-scale optimization of hierarchical features for saliency prediction in natural images," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (IEEE), 2798-2805
99. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371-3408
100. Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2014). Show and Tell: A Neural Image Caption Generator. arXiv preprint arXiv:1411.4555
101. Wan, L., Zeiler, M., Zhang, S., Cun, Y. L., and Fergus, R. (2013). "Regularization of neural networks using dropconnect," in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 1058-1066
102. Wang, A., Lu, J., Wang, G., Cai, J., and Cham, T.-J. (2014a). "Multi-modal unsupervised feature learning for Rgb-D scene labeling," in Computer Vision-ECCV 2014, volume 8693 of Lecture Notes in Computer Science, eds D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars (Springer), 453-467
103. Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., et al. (2014b). "Learning fine-grained image similarity with deep ranking," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 (IEEE), 1386-1393
104. Wang, H., and Schmid, C. (2013). Action recognition with improved trajectories. In IEEE International Conference on Computer Vision, Sydney
105. Wang, L., Qiao, Y., and Tang, X. (2015a). "Action recognition with trajectory-pooled deep-convolutional descriptors," in CVPR, 4305-4314
106. Wang, X., Zhang, L., Lin, L., Liang, Z., and Zuo, W. (2015b). Deep joint task learning for generic object extraction. CoRR, abs/1502.00743
107. Wang, S., and Manning, C. (2013). "Fast dropout training," in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 118-126
108. Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., et al. (2014). CNN: Single-Label to Multi-Label. CoRR, abs/1406.5726
109. Weinzaepfel, P., Revaud, J., Harchaoui, Z., and Schmid, C. (2013). "Deepflow: Large displacement optical flow with deep matching," in IEEE International Conference on Computer Vision (ICCV), 2013 (IEEE), 1385-1392
110. Yang, J., Jiang, Y.-G., Hauptmann, A. G., and Ngo, C.-W. (2007). "Evaluating bag-of-visual-words representations in scene classification," in Proceedings of the International Workshop on Multimedia Information Retrieval (ACM), 197-206
111. Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). "How transferable are features in deep neural networks?," in Advances in Neural Information Processing Systems, 27, eds Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger (Curran Associates, Inc.), 3320-3328
112. Zagoruyko, S., and Komodakis, N. (2015). Learning to Compare Image Patches via Convolutional Neural Networks. arXiv preprint arXiv:1504.03641
113. Zeiler, M., and Fergus, R. (2014). "Visualizing and understanding convolutional networks," in Computer Vision-ECCV 2014, volume 8689 of Lecture Notes in Computer Science, eds D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars (Springer International Publishing), 818-833. doi:10.1007/978-3-319-10590-1_53
114. Zeiler, M. D. (2012). Adadelta: An Adaptive Learning Rate Method. arXiv preprint arXiv:1212.5701
115. Zhang, Z., Luo, P., Loy, C. C., and Tang, X. (2014). Learning and Transferring Multi-Task Deep Representation for Face Alignment. CoRR, abs/1408.3967
116. Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., et al. (2015). Conditional Random Fields as Recurrent Neural Networks. arXiv preprint arXiv:1502.03240
117. Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., and Oliva, A. (2014). "Learning deep features for scene recognition using places database," in Advances in Neural Information Processing Systems 27, eds Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger (Curran Associates, Inc.), 487-495