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Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013a) · 1929
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Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013b) · 1929
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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H. and Wiesel, T. N. (1962) · 1962
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Some methods of speeding up the convergence of iteration methods
Polyak, B. T. (1964) · 1964
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Fukushima, K. (1980) · 1980
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A method of solving a convex programming problem with convergence rate o (1/k2)
Nesterov, Y. (1983) · 1983
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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1988) · 1988
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Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · 1991
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Signature verification using a “siamese” time delay neural network
Bromley, J., Bentz, J. W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., and Shah, R. (1993) · 1993
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Original approach for the localisation of objects in images
Vaillant, R., Monrocq, C., and Le Cun, Y. (1994) · 1994
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Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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Neural network-based face detection
Rowley, H. A., Baluja, S., and Kanade, T. (1998) · 1998
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Training products of experts by minimizing contrastive divergence
Hinton, G. E. (2002) · 2002
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Lucas-kanade 20 years on: A unifying framework
Baker, S. and Matthews, I. (2004) · 2004
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Distinctive image features from scale-invariant keypoints
Lowe, D. G. (2004) · 2004
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., and LeCun, Y. (2005) · 2005
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Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B. (2005) · 2005
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Bishop, C. M. (2006) · 2006
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006) · 2006
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A biologically inspired system for action recognition
Jhuang, H., Serre, T., Wolf, L., and Poggio, T. (2007) · 2007
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Evaluating bag-of-visual-words representations in scene classification
Yang, J., Jiang, Y.-G., Hauptmann, A. G., and Ngo, C.-W. (2007) · 2007
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Learning deep architectures for ai
Bengio, Y. (2009) · 2009
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A novel connectionist system for unconstrained handwriting recognition
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J. (2009) · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E. (2010) · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y. (2011) · 2011
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Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. (2011) · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. (2012) · 2012
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Aggregating local image descriptors into compact codes
Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., and Schmid, C. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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Convolutional-recursive deep learning for 3d object classification
Socher, R., Huval, B., Bath, B., Manning, C. D., and Ng, A. Y. (2012) · 2012
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Adadelta: An adaptive learning rate method
Zeiler, M. D. (2012) · 2012
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Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2013) · 2013
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Indoor semantic segmentation using depth information
Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013) · 2013
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Predicting parameters in deep learning
Denil, M., Shakibi, B., Dinh, L., Ranzato, M. A., and de Freitas, N. (2013) · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2013) · 2013
Cited alongside, same era.
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013) · 2013
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-r., and Hinton, G. (2013) · 2013
Cited alongside, same era.
Cnn features off-the-shelf: an astounding baseline for recognition
Razavian, A. S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014) · 2014
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., and Bengio, Y. (2014) · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014) · 2014
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Deepface: Closing the gap to human-level performance in face verification
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014) · 2014
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Translating videos to natural language using deep recurrent neural networks
Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., and Saenko, K. (2014) · 2014
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3d convolutional neural networks for human action recognition
Ji, S., Xu, W., Yang, M., and Yu, K. (2013) · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013) · 2013
Cited alongside, same era.
Pedestrian detection with unsupervised multi-stage feature learning
Sermanet, P., Kavukcuoglu, K., Chintala, S., and LeCun, Y. (2013c) · 2013
Cited alongside, same era.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013) · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013) · 2013
Cited alongside, same era.
Selective search for object recognition
Uijlings, J., van de Sande, K., Gevers, T., and Smeulders, A. (2013) · 2013
Cited alongside, same era.
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Large-scale optimization of hierarchical features for saliency prediction in natural images
Vig, E., Dorr, M., and Cox, D. (2014) · 2014
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2014) · 2014
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Multi-modal unsupervised feature learning for rgb-d scene labeling
Wang, A., Lu, J., Wang, G., Cai, J., and Cham, T.-J. (2014a) · 2014
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Learning fine-grained image similarity with deep ranking
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., and Wu, Y. (2014b) · 2014
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CNN: single-label to multi-label
Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., and Yan, S. (2014) · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014) · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. and Fergus, R. (2014) · 2014
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Learning and transferring multi-task deep representation for face alignment
Zhang, Z., Luo, P., Loy, C. C., and Tang, X. (2014) · 2014
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Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., and Oliva, A. (2014) · 2014
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Vqa: Visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., and Parikh, D. (2015) · 2015
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Are you talking to a machine? dataset and methods for multilingual image question answering
Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., and Xu, W. (2015) · 2015
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Girshick, R. B. (2015) · 2015
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Finding action tubes
Gkioxari, G. and Malik, J. (2015) · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
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Deepfix: A fully convolutional neural network for predicting human eye fixations
Kruthiventi, S. S., Ayush, K., and Babu, R. V. (2015) · 2015
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Deep convolutional inverse graphics network
Kulkarni, T. D., Whitney, W., Kohli, P., and Tenenbaum, J. B. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Predicting eye fixations using convolutional neural networks
Liu, N., Han, J., Zhang, D., Wen, S., and Liu, T. (2015) · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T. (2015) · 2015
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Ask your neurons: A neural-based approach to answering questions about images
Malinowski, M., Rohrbach, M., and Fritz, M. (2015) · 2015
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Learning like a child: Fast novel visual concept learning from sentence descriptions of images
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. (2015) · 2015
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Object level deep feature pooling for compact image representation
Mopuri, K. and Babu, R. (2015) · 2015
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Jointly modeling embedding and translation to bridge video and language
Pan, Y., Mei, T., Yao, T., Li, H., and Rui, Y. (2015) · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., and Fei-Fei, L. (2015) · 2015
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Deep learning in neural networks: An overview
Schmidhuber, J. (2015) · 2015
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Data-free parameter pruning for deep neural networks
Srinivas, S. and Babu, R. V. (2015) · 2015
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Sequence to sequence–video to text
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., and Saenko, K. (2015) · 2015
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Learning to compare image patches via convolutional neural networks
Zagoruyko, S. and Komodakis, N. (2015) · 2015
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Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P. (2015) · 2015
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