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This work presents an unsupervised learning based approach to the ubiquitous computer vision problem of image matching.
Learning temporally persistent hierarchical representations
Becker, S.: · 1997
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Inversion of feedforward neural networks: Algorithms and applications
Jensen, C., Reed, R.D., Marks, R.J., El-Sharkawi, M., Jung, J.B., Miyamoto, R.T., Anderson, G.M., Eggen, C.J., et al.: · 1999
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Slow feature analysis: Unsupervised learning of invariances
Wiskott, L., Sejnowski, T.J.: · 2002
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Reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R.: · 2006
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Unsupervised learning of image transformations
Memisevic, R., Hinton, G.: · 2007
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Learning OpenCV: Computer vision with the OpenCV library
Bradski, G., Kaehler, A.: · 2008
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Deep learning from temporal coherence in video
Mobahi, H., Collobert, R., Weston, J.: · 2009
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Robust multiperson tracking from a mobile platform
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: · 2009
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Convolutional learning of spatio-temporal features
Taylor, G.W., Fergus, R., LeCun, Y., Bregler, C.: · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X., Bengio, Y.: · 2010
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Adaptive real-time video-tracking for arbitrary objects
Klein, D.A., Schulz, D., Frintrop, S., Cremers, A.B.: · 2010
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Large displacement optical flow: Descriptor matching in variational motion estimation
Brox, T., Malik, J.: · 2011
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A database and evaluation methodology for optical flow
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: · 2011
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Learning to align from scratch
Huang, G., Mattar, M., Lee, H., Learned-Miller, E.G.: · 2012
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A naturalistic open source movie for optical flow evaluation
Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A.: · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: · 2013
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Deepflow: Large displacement optical flow with deep matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: · 2013
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Do convnets learn correspondence?
Long, J.L., Zhang, N., Darrell, T.: · 2014
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Descriptor matching with convolutional neural networks: a comparison to sift
Fischer, P., Dosovitskiy, A., Brox, T.: · 2014
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Video (language) modeling: a baseline for generative models of natural videos
Discriminative learning of deep convolutional feature point descriptors
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: · 2015
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Computing the stereo matching cost with a convolutional neural network
Žbontar, J., LeCun, Y.: · 2015
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Leveraging stereo matching with learning-based confidence measures
Park, M.G., Yoon, K.J.: · 2015
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Learning to compare image patches via convolutional neural networks
Zagoruyko, S., Komodakis, N.: · 2015
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Flownet: Learning optical flow with convolutional networks
Fischer, P., Dosovitskiy, A., Ilg, E., Häusser, P., Hazırbaş, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: · 2015
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Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., Chopra, S.: · 2014
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Understanding deep image representations by inverting them
Mahendran, A., Vedaldi, A.: · 2014
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Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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Matconvnet-convolutional neural networks for matlab
Vedaldi, A., Lenc, K.: · 2014
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
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A quantitative analysis of current practices in optical flow estimation and the principles behind them
Sun, D., Roth, S., Black, M.J.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Wang, X., Gupta, A.: · 2015
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Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., Salakhutdinov, R.: · 2015
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Learning to linearize under uncertainty
Goroshin, R., Mathieu, M., LeCun, Y.: · 2015
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Inverting convolutional networks with convolutional networks
Dosovitskiy, A., Brox, T.: · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: · 2015
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