{"list":"other","total":54,"rows":[{"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":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2102.09672","title":"Improved Denoising Diffusion Probabilistic Models","created":"2021-02-18","cited_by_count":3761},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"2012.09841","title":"Taming Transformers for High-Resolution Image Synthesis","created":"2020-12-17","cited_by_count":3253},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"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":"1802.05957","title":"Spectral Normalization for Generative Adversarial Networks","created":"2018-02-16","cited_by_count":1636},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"2201.09865","title":"RePaint: Inpainting using Denoising Diffusion Probabilistic Models","created":"2022-01-24","cited_by_count":1430},{"id":"1506.03365","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","created":"2015-06-10","cited_by_count":1355},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"2006.06676","title":"Training Generative Adversarial Networks with Limited Data","created":"2020-06-11","cited_by_count":1186},{"id":"2006.09011","title":"Improved Techniques for Training Score-Based Generative Models","created":"2020-06-16","cited_by_count":1043},{"id":"2007.03898","title":"NVAE: A Deep Hierarchical Variational Autoencoder","created":"2020-07-08","cited_by_count":637},{"id":"1912.03263","title":"Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One","created":"2019-12-06","cited_by_count":405},{"id":"1906.02735","title":"Residual Flows for Invertible Generative Modeling","created":"2019-06-06","cited_by_count":257},{"id":"1602.03264","title":"A Theory of Generative ConvNet","created":"2016-02-10","cited_by_count":174},{"id":"2207.06635","title":"EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations","created":"2022-07-14","cited_by_count":172},{"id":"1611.03852","title":"A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models","created":"2016-11-11","cited_by_count":168},{"id":"2012.01316","title":"Improved Contrastive Divergence Training of Energy Based Models","created":"2020-12-02","cited_by_count":130},{"id":"2012.08125","title":"Learning Energy-Based Models by Diffusion Recovery Likelihood","created":"2020-12-15","cited_by_count":123},{"id":"1609.03126","title":"Energy-based Generative Adversarial Network","created":"2016-09-11","cited_by_count":119},{"id":"2104.03310","title":"Regularizing Generative Adversarial Networks under Limited Data","created":"2021-04-07","cited_by_count":107},{"id":"2010.00654","title":"VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models","created":"2020-10-01","cited_by_count":104},{"id":"1912.00589","title":"Flow Contrastive Estimation of Energy-Based Models","created":"2019-12-02","cited_by_count":75},{"id":"1609.09408","title":"Cooperative Training of Descriptor and Generator Networks","created":"2016-09-29","cited_by_count":60},{"id":"1901.08508","title":"Maximum Entropy Generators for Energy-Based Models","created":"2019-01-24","cited_by_count":56},{"id":"2205.06924","title":"A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model","created":"2022-05-13","cited_by_count":44},{"id":"2012.14936","title":"Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler","created":"2020-12-29","cited_by_count":43},{"id":"1606.03439","title":"Deep Directed Generative Models with Energy-Based Probability Estimation","created":"2016-06-10","cited_by_count":34},{"id":"1709.08868","title":"Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling","created":"2017-09-26","cited_by_count":32},{"id":"1711.02282","title":"Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net","created":"2017-11-07","cited_by_count":30},{"id":"2004.02088","title":"Feature Quantization Improves GAN Training","created":"2020-04-05","cited_by_count":23},{"id":"1711.08875","title":"Wasserstein Introspective Neural Networks","created":"2017-11-24","cited_by_count":18},{"id":"2209.08739","title":"Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model","created":"2022-09-19","cited_by_count":18},{"id":"2303.03023","title":"Guiding Energy-based Models via Contrastive Latent Variables","created":"2023-03-06","cited_by_count":18},{"id":"2203.13455","title":"A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training","created":"2022-03-25","cited_by_count":13},{"id":"2210.16486","title":"Learning Probabilistic Models from Generator Latent Spaces with Hat EBM","created":"2022-10-29","cited_by_count":11},{"id":"2012.06568","title":"Learning Energy-Based Models With Adversarial Training","created":"2020-12-11","cited_by_count":9},{"id":"2302.01384","title":"Energy-Inspired Self-Supervised Pretraining for Vision Models","created":"2023-02-02","cited_by_count":7},{"id":"2203.14702","title":"Bi-level Doubly Variational Learning for Energy-based Latent Variable Models","created":"2022-03-24","cited_by_count":6}],"next_cursor":null}