{"list":"other","total":64,"rows":[{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"2102.12092","title":"Zero-Shot Text-to-Image Generation","created":"2021-02-24","cited_by_count":4288},{"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":"1705.07750","title":"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","created":"2017-05-22","cited_by_count":3270},{"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":"1906.00446","title":"Generating Diverse High-Fidelity Images with VQ-VAE-2","created":"2019-06-02","cited_by_count":1387},{"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":"1903.07291","title":"Semantic Image Synthesis with Spatially-Adaptive Normalization","created":"2019-03-18","cited_by_count":1299},{"id":"1801.01401","title":"Demystifying MMD GANs","created":"2018-01-04","cited_by_count":1293},{"id":"1904.06991","title":"Improved Precision and Recall Metric for Assessing Generative Models","created":"2019-04-15","cited_by_count":944},{"id":"1606.05328","title":"Conditional Image Generation with PixelCNN Decoders","created":"2016-06-16","cited_by_count":862},{"id":"1912.11035","title":"CNN-generated images are surprisingly easy to spot... for now","created":"2019-12-23","cited_by_count":847},{"id":"1707.04993","title":"MoCoGAN: Decomposing Motion and Content for Video Generation","created":"2017-07-17","cited_by_count":524},{"id":"2003.08685","title":"Leveraging Frequency Analysis for Deep Fake Image Recognition","created":"2020-03-19","cited_by_count":504},{"id":"2002.09797","title":"Reliable Fidelity and Diversity Metrics for Generative Models","created":"2020-02-23","cited_by_count":434},{"id":"1806.00035","title":"Assessing Generative Models via Precision and Recall","created":"2018-05-31","cited_by_count":406},{"id":"1511.01844","title":"A note on the evaluation of generative models","created":"2015-11-05","cited_by_count":292},{"id":"2003.01826","title":"Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions","created":"2020-03-03","cited_by_count":280},{"id":"2008.10588","title":"What makes fake images detectable? Understanding properties that generalize","created":"2020-08-24","cited_by_count":273},{"id":"1711.10337","title":"Are GANs Created Equal? A Large-Scale Study","created":"2017-11-28","cited_by_count":267},{"id":"2103.03841","title":"Generating Images with Sparse Representations","created":"2021-03-05","cited_by_count":266},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252},{"id":"1907.07171","title":"On the \"steerability\" of generative adversarial networks","created":"2019-07-16","cited_by_count":248},{"id":"2001.00179","title":"DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection","created":"2020-01-01","cited_by_count":233},{"id":"1801.01973","title":"A Note on the Inception Score","created":"2018-01-06","cited_by_count":226},{"id":"1802.03446","title":"Pros and Cons of GAN Evaluation Measures","created":"2018-02-09","cited_by_count":224},{"id":"1506.05751","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","created":"2015-06-18","cited_by_count":214},{"id":"2102.08921","title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","created":"2021-02-17","cited_by_count":196},{"id":"1803.09518","title":"Fr\\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery","created":"2018-03-26","cited_by_count":194},{"id":"1911.07023","title":"Effectively Unbiased FID and Inception Score and where to find them","created":"2019-11-16","cited_by_count":165},{"id":"1805.11202","title":"FairGAN: Fairness-aware Generative Adversarial Networks","created":"2018-05-28","cited_by_count":164},{"id":"1803.10404","title":"Lip Movements Generation at a Glance","created":"2018-03-28","cited_by_count":155},{"id":"1910.11626","title":"Seeing What a GAN Cannot Generate","created":"2019-10-24","cited_by_count":148},{"id":"1911.06465","title":"Fourier Spectrum Discrepancies in Deep Network Generated Images","created":"2019-11-15","cited_by_count":139},{"id":"1905.10887","title":"Classification Accuracy Score for Conditional Generative Models","created":"2019-05-26","cited_by_count":136},{"id":"2103.01209","title":"Generative Adversarial Transformers","created":"2021-03-01","cited_by_count":120},{"id":"2004.05214","title":"A Review on Deep Learning Techniques for Video Prediction","created":"2020-04-10","cited_by_count":114},{"id":"1811.03259","title":"Bias and Generalization in Deep Generative Models: An Empirical Study","created":"2018-11-08","cited_by_count":83},{"id":"1807.09499","title":"How good is my GAN?","created":"2018-07-25","cited_by_count":74},{"id":"1807.11152","title":"Pose Guided Human Video Generation","created":"2018-07-30","cited_by_count":67},{"id":"1904.01121","title":"HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models","created":"2019-04-01","cited_by_count":59},{"id":"1802.02664","title":"Geometry Score: A Method For Comparing Generative Adversarial Networks","created":"2018-02-07","cited_by_count":57},{"id":"2004.05675","title":"A Non-Parametric Test to Detect Data-Copying in Generative Models","created":"2020-04-12","cited_by_count":52},{"id":"2008.02793","title":"Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications","created":"2020-08-06","cited_by_count":48},{"id":"2004.03355","title":"Inclusive GAN: Improving Data and Minority Coverage in Generative Models","created":"2020-04-07","cited_by_count":44},{"id":"1905.11141","title":"The Shape of Data: Intrinsic Distance for Data Distributions","created":"2019-05-27","cited_by_count":38},{"id":"1805.09910","title":"Fairness GAN","created":"2018-05-24","cited_by_count":32},{"id":"2001.03653","title":"Towards GAN Benchmarks Which Require Generalization","created":"2020-01-10","cited_by_count":31},{"id":"1803.07474","title":"An Improved Evaluation Framework for Generative Adversarial Networks","created":"2018-03-20","cited_by_count":26},{"id":"1906.11891","title":"Characterizing Bias in Classifiers using Generative Models","created":"2019-05-30","cited_by_count":23},{"id":"2005.02494","title":"Mimicry: Towards the Reproducibility of GAN Research","created":"2020-05-05","cited_by_count":14},{"id":"1905.05441","title":"Revisiting Precision and Recall Definition for Generative Model Evaluation","created":"2019-05-14","cited_by_count":9},{"id":"1811.04172","title":"Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation","created":"2018-11-10","cited_by_count":7},{"id":"1810.12686","title":"Evaluating Text GANs as Language Models","created":"2018-10-30","cited_by_count":5},{"id":"1905.00643","title":"Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality","created":"2019-05-02","cited_by_count":5},{"id":"2012.04027","title":"Generating unseen complex scenes: are we there yet?","created":"2020-12-07","cited_by_count":5},{"id":"1708.02688","title":"Statistics of Deep Generated Images","created":"2017-08-09","cited_by_count":4},{"id":"1912.12121","title":"Approximating Human Judgment of Generated Image Quality","created":"2019-11-30","cited_by_count":4},{"id":"1909.12638","title":"On the Anomalous Generalization of GANs","created":"2019-09-27","cited_by_count":2},{"id":"1812.10782","title":"Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions","created":"2018-12-27","cited_by_count":1}],"next_cursor":null}