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Thomas S. Huang | Yulun Zhang | Jiahui Yu | Yun Fu | Yuchen Fan | Yiqun Mei | Ding Liu | Thomas S. Huang | Y. Fu | Jiahui Yu | Ding Liu | Yulun Zhang | Yuchen Fan | Yiqun Mei
[1] Michael Carbin,et al. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks , 2018, ICLR.
[2] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[3] Wen Gao,et al. Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior , 2017, IEEE Transactions on Circuits and Systems for Video Technology.
[4] Aline Roumy,et al. Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding , 2012, BMVC.
[5] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Lei Zhang,et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.
[7] Ning Xu,et al. Slimmable Neural Networks , 2018, ICLR.
[8] Michael Elad,et al. Image Denoising Via Learned Dictionaries and Sparse representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[9] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[10] Thomas S. Huang,et al. Coupled Dictionary Training for Image Super-Resolution , 2012, IEEE Transactions on Image Processing.
[11] Xiaoou Tang,et al. Compression Artifacts Reduction by a Deep Convolutional Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[12] Xiaoou Tang,et al. Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.
[13] Quoc V. Le,et al. CondConv: Conditionally Parameterized Convolutions for Efficient Inference , 2019, NeurIPS.
[14] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[15] Alessandro Foi,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.
[16] Lei Zhang,et al. Weighted Nuclear Norm Minimization with Application to Image Denoising , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Narendra Ahuja,et al. Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Alan C. Bovik,et al. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.
[19] Kai Yu,et al. Reshaping deep neural network for fast decoding by node-pruning , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[20] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[21] Karen O. Egiazarian,et al. Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images , 2007, IEEE Transactions on Image Processing.
[22] Thomas S. Huang,et al. Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.
[23] Michael Elad,et al. Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.
[24] Changshui Zhang,et al. Sparse DNNs with Improved Adversarial Robustness , 2018, NeurIPS.
[25] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[26] Kyoung Mu Lee,et al. Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[27] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[28] Luc Van Gool,et al. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[29] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Michael Elad,et al. Learning Multiscale Sparse Representations for Image and Video Restoration , 2007, Multiscale Model. Simul..
[31] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[32] Michael Elad,et al. On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.
[33] Yun Fu,et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.
[34] Kyoung Mu Lee,et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Jiahui Yu,et al. Wide Activation for Efficient Image and Video Super-Resolution , 2019, BMVC.