{"list":"other","total":52,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"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":"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":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1610.02357","title":"Xception: Deep Learning with Depthwise Separable Convolutions","created":"2016-10-07","cited_by_count":2332},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1901.08971","title":"FaceForensics++: Learning to Detect Manipulated Facial Images","created":"2019-01-25","cited_by_count":1101},{"id":"1912.01865","title":"StarGAN v2: Diverse Image Synthesis for Multiple Domains","created":"2019-12-04","cited_by_count":1098},{"id":"1711.09020","title":"StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation","created":"2017-11-24","cited_by_count":1039},{"id":"1912.11035","title":"CNN-generated images are surprisingly easy to spot... for now","created":"2019-12-23","cited_by_count":847},{"id":"1703.10593","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","created":"2017-03-30","cited_by_count":707},{"id":"2003.00196","title":"First Order Motion Model for Image Animation","created":"2020-02-29","cited_by_count":704},{"id":"2007.14808","title":"Face2Face: Real-time Face Capture and Reenactment of RGB Videos","created":"2020-07-29","cited_by_count":632},{"id":"1907.11922","title":"MaskGAN: Towards Diverse and Interactive Facial Image Manipulation","created":"2019-07-27","cited_by_count":628},{"id":"1909.12962","title":"Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics","created":"2019-09-27","cited_by_count":575},{"id":"2110.08263","title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","created":"2021-10-15","cited_by_count":540},{"id":"2007.09355","title":"Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues","created":"2020-07-18","cited_by_count":523},{"id":"2103.02406","title":"Multi-attentional Deepfake Detection","created":"2021-03-03","cited_by_count":341},{"id":"2204.08376","title":"Detecting Deepfakes with Self-Blended Images","created":"2022-04-18","cited_by_count":338},{"id":"1908.05932","title":"FSGAN: Subject Agnostic Face Swapping and Reenactment","created":"2019-08-16","cited_by_count":320},{"id":"2012.07657","title":"Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection","created":"2020-12-14","cited_by_count":287},{"id":"1910.01717","title":"On the Detection of Digital Face Manipulation","created":"2019-10-03","cited_by_count":260},{"id":"1905.03820","title":"Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss","created":"2019-05-09","cited_by_count":252},{"id":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232},{"id":"1912.13457","title":"FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping","created":"2019-12-31","cited_by_count":221},{"id":"2201.02609","title":"Generalized Category Discovery","created":"2022-01-07","cited_by_count":211},{"id":"2007.08457","title":"Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data","created":"2020-07-16","cited_by_count":201},{"id":"2012.09311","title":"Learning Self-Consistency for Deepfake Detection","created":"2020-12-16","cited_by_count":179},{"id":"2102.03526","title":"Open-World Semi-Supervised Learning","created":"2021-02-06","cited_by_count":165},{"id":"1805.04953","title":"Learning Rich Features for Image Manipulation Detection","created":"2018-05-13","cited_by_count":154},{"id":"2109.00650","title":"Dash: Semi-Supervised Learning with Dynamic Thresholding","created":"2021-09-01","cited_by_count":148},{"id":"2002.05714","title":"Automatically Discovering and Learning New Visual Categories with Ranking Statistics","created":"2020-02-13","cited_by_count":143},{"id":"1711.10125","title":"Learning to cluster in order to transfer across domains and tasks","created":"2017-11-28","cited_by_count":128},{"id":"2103.05630","title":"ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis","created":"2021-03-09","cited_by_count":126},{"id":"1902.06838","title":"SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color","created":"2019-02-18","cited_by_count":106},{"id":"2105.02577","title":"Local Relation Learning for Face Forgery Detection","created":"2021-05-06","cited_by_count":100},{"id":"2112.13522","title":"Dual Contrastive Learning for General Face Forgery Detection","created":"2021-12-27","cited_by_count":100},{"id":"2109.01860","title":"Spatiotemporal Inconsistency Learning for DeepFake Video Detection","created":"2021-09-04","cited_by_count":88},{"id":"2105.04580","title":"Towards Discovery and Attribution of Open-world GAN Generated Images","created":"2021-05-10","cited_by_count":66},{"id":"2112.13977","title":"Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning","created":"2021-12-28","cited_by_count":60},{"id":"2202.13843","title":"Deepfake Network Architecture Attribution","created":"2022-02-28","cited_by_count":47},{"id":"2203.08519","title":"Towards Practical Certifiable Patch Defense with Vision Transformer","created":"2022-03-16","cited_by_count":44},{"id":"2207.02261","title":"OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning","created":"2022-07-05","cited_by_count":37},{"id":"2207.09679","title":"Explaining Deepfake Detection by Analysing Image Matching","created":"2022-07-20","cited_by_count":34},{"id":"2202.12972","title":"FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment","created":"2022-02-25","cited_by_count":33},{"id":"2207.02269","title":"Towards Realistic Semi-Supervised Learning","created":"2022-07-05","cited_by_count":27},{"id":"2204.04513","title":"On the Exploitation of Deepfake Model Recognition","created":"2022-04-09","cited_by_count":14}],"next_cursor":null}