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On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
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R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Processing , vol. 61, no. 3, pp. 661–677, 2013
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Z. Wang, J. Yang, N. Nasrabadi, and T. Huang, “A max-margin perspective on sparse representation-based classification,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 1217–1224
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2014
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E. Chouzenoux, J.-C. Pesquet, and A. Repetti, “Variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function,” Journal of Optimization Theory and Applications , vol. 162, no. 1, pp. 107–132, Jul. 2014
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2014
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2017
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2017
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Q. Wang, Y. Guo, J. Guo, and X. Kong, “Synthesis k-svd based analysis dictionary learning for pattern classification,” Multimedia Tools and Applications , vol. 77, no. 13, pp. 17 023–17 041, 2018
2018
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W. Tang, A. Panahi, H. Krim, and L. Dai, “Structured analysis dictionary learning for image classification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 2181–2185
2018
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2015
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Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang, “Deep networks for image super-resolution with sparse prior,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 370–378
2015
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M. Xu, X. Jia, M. Pickering, and A. J. Plaza, “Cloud removal based on sparse representation via multitemporal dictionary learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 54, no. 5, pp. 2998–3006, 2016. [Online]. Available: https://app.dimensions.ai/details/publication/pub.1061614193
2016
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E. Skau, B. Wohlberg, H. Krim, and L. Dai, “Pansharpening via coupled triple factorization dictionary learning,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 1234–1237
2016
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X. Bian, H. Krim, A. Bronstein, and L. Dai, “Sparsity and nullity: Paradigms for analysis dictionary learning,” SIAM Journal on Imaging Sciences , vol. 9, no. 3, pp. 1107–1126, 2016
2016
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W. Tang, I. R. Otero, H. Krim, and L. Dai, “Analysis dictionary learning for scene classification,” in Statistical Signal Processing Workshop (SSP), 2016 IEEE . IEEE, 2016, pp. 1–5
2016
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J. Guo, Y. Guo, X. Kong, M. Zhang, and R. He, “Discriminative analysis dictionary learning,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
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S. Tariyal, A. Majumdar, R. Singh, and M. Vatsa, “Deep dictionary learning,” IEEE Access , vol. 4, pp. 10 096–10 109, 2016
2016
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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, pp. 770–778
2016
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J. T. Zhou, K. Di, J. Du, X. Peng, H. Yang, S. J. Pan, I. W. Tsang, Y. Liu, Z. Qin, and R. S. M. Goh, “Sc2net: Sparse lstms for sparse coding,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
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J.-J. Huang and P. L. Dragotti, “A deep dictionary model for image super-resolution,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’18) . Calgary, Canada: IEEE, March 2018
2018
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J. Maggu and A. Majumdar, “Unsupervised deep transform learning,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Calgary, Canada, 15-20 April 2018, pp. 6782–6786
2018
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Y. Liu, Q. Chen, W. Chen, and I. Wassell, “Dictionary learning inspired deep network for scene recognition,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
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——, “Analysis dictionary learning: an efficient and discriminative solution,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 3682–3686
2019
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——, “Analysis dictionary learning based classification: Structure for robustness,” IEEE Transactions on Image Processing , vol. 28, no. 12, pp. 6035–6046, 2019
2019
Later among the works it cites.
S. Mahdizadehaghdam, A. Panahi, H. Krim, and L. Dai, “Deep dictionary learning: A parametric network approach,” IEEE Transactions on Image Processing , 2019
2019
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P. Gupta, J. Maggu, A. Majumdar, E. Chouzenoux, and G. Chierchia, “Deconfuse: A deep convolutional transform based unsupervised fusion framework,” Tech. Rep., 2020, https://hal.archives-ouvertes.fr/hal-02461768
2020
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M. Hasannasab, J. Hertrich, S. Neumayer, G. Plonka, S. Setzer, and G. Steidl, “Parseval proximal neural networks,” Journal of Fourier Analysis and Applications , vol. 26, no. 4, pp. 1–31, 2020
2020
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P. L. Combettes and J.-C. Pesquet, “Deep neural network structures solving variational inequalities,” Set-Valued and Variational Analysis , pp. 1–28, 2020
2020
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