Boltzmann Machines for Image Denoising
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Publisher
Springer Berlin Heidelberg
Link
http://link.springer.com/content/pdf/10.1007/978-3-642-40728-4_76
Reference17 articles.
1. Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cognitive Science 9, 147–169 (1985)
2. Burger, H., Schuler, C., Harmeling, S.: Image denoising: Can plain neural networks compete with bm3d? In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399 (June 2012)
3. Cho, K., Raiko, T., Ilin, A.: Enhanced gradient for training restricted Boltzmann machines. Neural Computation 25(3), 805–831 (2013)
4. Cho, K., Raiko, T., Ilin, A., Karhunen, J.: A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines. In: NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning, Lake Tahoe (December 2012)
5. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736–3745 (2006)
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