{"list":"other","total":51,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1608.03981","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","created":"2016-08-13","cited_by_count":1358},{"id":"1806.08734","title":"On the Spectral Bias of Neural Networks","created":"2018-06-22","cited_by_count":1341},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1511.07289","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","created":"2015-11-23","cited_by_count":1070},{"id":"2007.03898","title":"NVAE: A Deep Hierarchical Variational Autoencoder","created":"2020-07-08","cited_by_count":637},{"id":"1710.04026","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","created":"2017-10-11","cited_by_count":510},{"id":"1704.03264","title":"Learning Deep CNN Denoiser Prior for Image Restoration","created":"2017-04-11","cited_by_count":408},{"id":"1806.00035","title":"Assessing Generative Models via Precision and Recall","created":"2018-05-31","cited_by_count":406},{"id":"2003.03808","title":"PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models","created":"2020-03-08","cited_by_count":358},{"id":"1703.03208","title":"Compressed Sensing using Generative Models","created":"2017-03-09","cited_by_count":339},{"id":"1611.02862","title":"The Little Engine that Could: Regularization by Denoising (RED)","created":"2016-11-09","cited_by_count":307},{"id":"2011.10650","title":"Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images","created":"2020-11-20","cited_by_count":264},{"id":"1605.01710","title":"Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications","created":"2016-05-05","cited_by_count":246},{"id":"1907.02544","title":"Large Scale Adversarial Representation Learning","created":"2019-07-04","cited_by_count":239},{"id":"1905.05406","title":"Plug-and-Play Methods Provably Converge with Properly Trained Denoisers","created":"2019-05-14","cited_by_count":198},{"id":"1508.02848","title":"Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration","created":"2015-08-12","cited_by_count":195},{"id":"1903.05789","title":"Diagnosing and Enhancing VAE Models","created":"2019-03-14","cited_by_count":193},{"id":"1911.02469","title":"Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse","created":"2019-11-06","cited_by_count":121},{"id":"2003.13913","title":"Flows for simultaneous manifold learning and density estimation","created":"2020-03-31","cited_by_count":118},{"id":"1704.03488","title":"Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems","created":"2017-04-11","cited_by_count":107},{"id":"1806.02296","title":"Regularization by Denoising: Clarifications and New Interpretations","created":"2018-06-06","cited_by_count":103},{"id":"1801.06724","title":"DeepISP: Towards Learning an End-to-End Image Processing Pipeline","created":"2018-01-20","cited_by_count":88},{"id":"1512.07331","title":"Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation","created":"2015-12-23","cited_by_count":78},{"id":"1709.01809","title":"CNN-Based Projected Gradient Descent for Consistent Image Reconstruction","created":"2017-09-06","cited_by_count":77},{"id":"2012.13247","title":"Learning Maximally Monotone Operators for Image Recovery","created":"2020-12-24","cited_by_count":62},{"id":"1705.08983","title":"Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium","created":"2017-05-24","cited_by_count":58},{"id":"1802.08406","title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","created":"2018-02-23","cited_by_count":58},{"id":"2008.00226","title":"Regularization by Denoising via Fixed-Point Projection (RED-PRO)","created":"2020-08-01","cited_by_count":55},{"id":"1705.06566","title":"Learning Texture Manifolds with the Periodic Spatial GAN","created":"2017-05-18","cited_by_count":53},{"id":"1306.0187","title":"Proximal Markov chain Monte Carlo algorithms","created":"2013-06-02","cited_by_count":46},{"id":"1511.06406","title":"Denoising Criterion for Variational Auto-Encoding Framework","created":"2015-11-19","cited_by_count":43},{"id":"1705.08041","title":"Unrolled Optimization with Deep Priors","created":"2017-05-22","cited_by_count":40},{"id":"2005.07685","title":"Provable Convergence of Plug-and-Play Priors with MMSE denoisers","created":"2020-05-15","cited_by_count":39},{"id":"1705.07576","title":"Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk","created":"2017-05-22","cited_by_count":37},{"id":"1709.03749","title":"Deep Mean-Shift Priors for Image Restoration","created":"2017-09-12","cited_by_count":34},{"id":"1907.03343","title":"Fast and Provable ADMM for Learning with Generative Priors","created":"2019-07-07","cited_by_count":18},{"id":"1812.04176","title":"A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors","created":"2018-12-11","cited_by_count":15},{"id":"1906.10335","title":"Perceptual Generative Autoencoders","created":"2019-06-25","cited_by_count":15},{"id":"1711.04915","title":"Adversarial Symmetric Variational Autoencoder","created":"2017-11-14","cited_by_count":11},{"id":"1901.03707","title":"Neumann Networks for Inverse Problems in Imaging","created":"2019-01-13","cited_by_count":9},{"id":"1703.09964","title":"Image Restoration using Autoencoding Priors","created":"2017-03-29","cited_by_count":8},{"id":"1706.03261","title":"A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging","created":"2017-06-10","cited_by_count":7},{"id":"1309.5032","title":"A primal-dual hybrid gradient method for non-linear operators with applications to MRI","created":"2013-09-19","cited_by_count":5},{"id":"1801.00605","title":"Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging","created":"2018-01-02","cited_by_count":5},{"id":"2101.08661","title":"Regularization via deep generative models: an analysis point of view","created":"2021-01-21","cited_by_count":5},{"id":"1511.00425","title":"Preconditioned ADMM with nonlinear operator constraint","created":"2015-11-02","cited_by_count":3},{"id":"2009.04583","title":"Blind Image Restoration with Flow Based Priors","created":"2020-09-09","cited_by_count":1}],"next_cursor":null}