{"list":"other","total":67,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"1809.11096","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","created":"2018-09-28","cited_by_count":2626},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1802.05957","title":"Spectral Normalization for Generative Adversarial Networks","created":"2018-02-16","cited_by_count":1636},{"id":"2106.12423","title":"Alias-Free Generative Adversarial Networks","created":"2021-06-23","cited_by_count":1184},{"id":"2010.05646","title":"HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis","created":"2020-10-12","cited_by_count":1154},{"id":"1805.08318","title":"Self-Attention Generative Adversarial Networks","created":"2018-05-21","cited_by_count":809},{"id":"1801.04406","title":"Which Training Methods for GANs do actually Converge?","created":"2018-01-13","cited_by_count":679},{"id":"1701.07875","title":"Wasserstein GAN","created":"2017-01-26","cited_by_count":639},{"id":"1707.04993","title":"MoCoGAN: Decomposing Motion and Content for Video Generation","created":"2017-07-17","cited_by_count":524},{"id":"1606.00709","title":"f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization","created":"2016-06-02","cited_by_count":506},{"id":"2202.00273","title":"StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets","created":"2022-02-01","cited_by_count":492},{"id":"1811.00995","title":"Invertible Residual Networks","created":"2018-11-02","cited_by_count":365},{"id":"1910.06711","title":"MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis","created":"2019-10-08","cited_by_count":354},{"id":"1802.05637","title":"cGANs with Projection Discriminator","created":"2018-02-15","cited_by_count":254},{"id":"1705.02894","title":"Geometric GAN","created":"2017-05-08","cited_by_count":233},{"id":"1802.04208","title":"Adversarial Audio Synthesis","created":"2018-02-12","cited_by_count":225},{"id":"2203.06026","title":"The Role of ImageNet Classes in Fr\\'echet Inception Distance","created":"2022-03-11","cited_by_count":213},{"id":"2301.09515","title":"StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis","created":"2023-01-23","cited_by_count":212},{"id":"1902.00434","title":"Generalized Sliced Wasserstein Distances","created":"2019-02-01","cited_by_count":208},{"id":"2111.01007","title":"Projected GANs Converge Faster","created":"2021-11-01","cited_by_count":208},{"id":"1705.07761","title":"VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning","created":"2017-05-22","cited_by_count":200},{"id":"1703.00573","title":"Generalization and Equilibrium in Generative Adversarial Nets (GANs)","created":"2017-03-02","cited_by_count":166},{"id":"1910.12027","title":"Consistency Regularization for Generative Adversarial Networks","created":"2019-10-26","cited_by_count":136},{"id":"1904.05877","title":"Max-Sliced Wasserstein Distance and its use for GANs","created":"2019-04-11","cited_by_count":119},{"id":"1705.10461","title":"The Numerics of GANs","created":"2017-05-30","cited_by_count":105},{"id":"2104.07659","title":"GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds","created":"2021-04-15","cited_by_count":98},{"id":"1806.04498","title":"The Unusual Effectiveness of Averaging in GAN Training","created":"2018-06-12","cited_by_count":73},{"id":"2002.07367","title":"Distributional Sliced-Wasserstein and Applications to Generative Modeling","created":"2020-02-18","cited_by_count":69},{"id":"2205.07547","title":"SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization","created":"2022-05-16","cited_by_count":68},{"id":"1706.04156","title":"Gradient descent GAN optimization is locally stable","created":"2017-06-13","cited_by_count":67},{"id":"1904.08598","title":"Reducing Noise in GAN Training with Variance Reduced Extragradient","created":"2019-04-18","cited_by_count":62},{"id":"2112.02424","title":"Variational Wasserstein gradient flow","created":"2021-12-04","cited_by_count":53},{"id":"1802.08249","title":"On the Convergence and Robustness of Training GANs with Regularized Optimal Transport","created":"2018-02-22","cited_by_count":51},{"id":"1811.02002","title":"Finding Mixed Nash Equilibria of Generative Adversarial Networks","created":"2018-10-23","cited_by_count":49},{"id":"1907.07945","title":"MintNet: Building Invertible Neural Networks with Masked Convolutions","created":"2019-07-18","cited_by_count":35},{"id":"1901.08469","title":"Deep Generative Learning via Variational Gradient Flow","created":"2019-01-24","cited_by_count":32},{"id":"2206.08780","title":"Spherical Sliced-Wasserstein","created":"2022-06-17","cited_by_count":31},{"id":"1907.05681","title":"Adversarial Lipschitz Regularization","created":"2019-07-12","cited_by_count":30},{"id":"2002.04185","title":"Smoothness and Stability in GANs","created":"2020-02-11","cited_by_count":27},{"id":"1906.04848","title":"A Closer Look at the Optimization Landscapes of Generative Adversarial Networks","created":"2019-06-11","cited_by_count":25},{"id":"2204.01188","title":"Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution","created":"2022-04-04","cited_by_count":25},{"id":"1910.09779","title":"Bridging the Gap Between $f$-GANs and Wasserstein GANs","created":"2019-10-22","cited_by_count":23},{"id":"2006.08464","title":"Globally Injective ReLU Networks","created":"2020-06-15","cited_by_count":23},{"id":"2009.02773","title":"Why Spectral Normalization Stabilizes GANs: Analysis and Improvements","created":"2020-09-06","cited_by_count":21},{"id":"2010.15040","title":"Training Generative Adversarial Networks by Solving Ordinary Differential Equations","created":"2020-10-28","cited_by_count":21},{"id":"2006.14567","title":"Taming GANs with Lookahead-Minmax","created":"2020-06-25","cited_by_count":16},{"id":"2006.08812","title":"Augmented Sliced Wasserstein Distances","created":"2020-06-15","cited_by_count":14},{"id":"2106.01488","title":"Minimax Optimization with Smooth Algorithmic Adversaries","created":"2021-06-02","cited_by_count":8},{"id":"1801.02227","title":"Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models","created":"2018-01-07","cited_by_count":5},{"id":"1909.13188","title":"Understanding and Stabilizing GANs' Training Dynamics with Control Theory","created":"2019-09-29","cited_by_count":5},{"id":"2002.06224","title":"Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples","created":"2020-02-14","cited_by_count":4}],"next_cursor":null}