{"list":"other","total":55,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"id":"1409.7495","title":"Unsupervised Domain Adaptation by Backpropagation","created":"2014-09-26","cited_by_count":1917},{"id":"1702.05464","title":"Adversarial Discriminative Domain Adaptation","created":"2017-02-17","cited_by_count":1311},{"id":"1801.09414","title":"CosFace: Large Margin Cosine Loss for Deep Face Recognition","created":"2018-01-29","cited_by_count":971},{"id":"1710.08092","title":"VGGFace2: A dataset for recognising faces across pose and age","created":"2017-10-23","cited_by_count":914},{"id":"1604.02878","title":"Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks","created":"2016-04-11","cited_by_count":903},{"id":"1704.08063","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","created":"2017-04-26","cited_by_count":737},{"id":"1801.07593","title":"Mitigating Unwanted Biases with Adversarial Learning","created":"2018-01-22","cited_by_count":663},{"id":"1607.08221","title":"MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition","created":"2016-07-27","cited_by_count":624},{"id":"1411.7923","title":"Learning Face Representation from Scratch","created":"2014-11-28","cited_by_count":453},{"id":"1710.06924","title":"VisDA: The Visual Domain Adaptation Challenge","created":"2017-10-18","cited_by_count":435},{"id":"1904.06487","title":"Semi-supervised Domain Adaptation via Minimax Entropy","created":"2019-04-13","cited_by_count":335},{"id":"2103.06627","title":"MagFace: A Universal Representation for Face Recognition and Quality Assessment","created":"2021-03-11","cited_by_count":334},{"id":"1801.05599","title":"Additive Margin Softmax for Face Verification","created":"2018-01-17","cited_by_count":278},{"id":"1804.06655","title":"Deep Face Recognition: A Survey","created":"2018-04-18","cited_by_count":276},{"id":"1704.06369","title":"NormFace: L2 Hypersphere Embedding for Face Verification","created":"2017-04-21","cited_by_count":237},{"id":"1803.09797","title":"Women also Snowboard: Overcoming Bias in Captioning Models","created":"2018-03-26","cited_by_count":236},{"id":"2103.04098","title":"WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition","created":"2021-03-06","cited_by_count":223},{"id":"1802.08735","title":"A DIRT-T Approach to Unsupervised Domain Adaptation","created":"2018-02-23","cited_by_count":215},{"id":"1911.10692","title":"Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning","created":"2019-11-25","cited_by_count":145},{"id":"2003.02488","title":"Demographic Bias in Biometrics: A Survey on an Emerging Challenge","created":"2020-03-05","cited_by_count":93},{"id":"1806.00194","title":"Deep Imbalanced Learning for Face Recognition and Attribute Prediction","created":"2018-06-01","cited_by_count":91},{"id":"1908.04913","title":"FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age","created":"2019-08-14","cited_by_count":87},{"id":"2007.10075","title":"Investigating Bias and Fairness in Facial Expression Recognition","created":"2020-07-20","cited_by_count":87},{"id":"2103.01592","title":"A Comprehensive Study on Face Recognition Biases Beyond Demographics","created":"2021-03-02","cited_by_count":86},{"id":"2002.06483","title":"Face Recognition: Too Bias, or Not Too Bias?","created":"2020-02-16","cited_by_count":82},{"id":"1901.10436","title":"Diversity in Faces","created":"2019-01-29","cited_by_count":77},{"id":"2006.07576","title":"Mitigating Face Recognition Bias via Group Adaptive Classifier","created":"2020-06-13","cited_by_count":67},{"id":"2003.09093","title":"Masked Face Recognition Dataset and Application","created":"2020-03-20","cited_by_count":66},{"id":"2007.06570","title":"Towards causal benchmarking of bias in face analysis algorithms","created":"2020-07-13","cited_by_count":64},{"id":"1912.07398","title":"Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?","created":"2019-12-16","cited_by_count":56},{"id":"1807.08379","title":"Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study","created":"2018-07-22","cited_by_count":48},{"id":"1904.05514","title":"Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach","created":"2019-04-11","cited_by_count":48},{"id":"2002.00065","title":"Analysis of Gender Inequality In Face Recognition Accuracy","created":"2020-01-31","cited_by_count":47},{"id":"2002.02934","title":"How Does Gender Balance In Training Data Affect Face Recognition Accuracy?","created":"2020-02-07","cited_by_count":37},{"id":"1603.07027","title":"MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes","created":"2016-03-22","cited_by_count":34},{"id":"2102.02320","title":"One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision","created":"2021-02-03","cited_by_count":33},{"id":"2004.06592","title":"InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics","created":"2020-04-14","cited_by_count":31},{"id":"1910.13268","title":"Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets","created":"2019-10-29","cited_by_count":29},{"id":"2002.03592","title":"Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization","created":"2020-02-10","cited_by_count":28},{"id":"2009.07838","title":"FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition","created":"2020-09-16","cited_by_count":26},{"id":"1812.00099","title":"Understanding Unequal Gender Classification Accuracy from Face Images","created":"2018-11-30","cited_by_count":25},{"id":"1904.01219","title":"Deep Learning for Face Recognition: Pride or Prejudiced?","created":"2019-04-02","cited_by_count":25},{"id":"2004.07173","title":"Bias in Multimodal AI: Testbed for Fair Automatic Recruitment","created":"2020-04-15","cited_by_count":21},{"id":"1610.03577","title":"Minimax Filter: Learning to Preserve Privacy from Inference Attacks","created":"2016-10-12","cited_by_count":16},{"id":"2011.13583","title":"An Ethical Highlighter for People-Centric Dataset Creation","created":"2020-11-27","cited_by_count":12},{"id":"2006.07845","title":"Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition","created":"2020-06-14","cited_by_count":11},{"id":"1807.07718","title":"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN","created":"2018-07-20","cited_by_count":10},{"id":"1807.11936","title":"Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers","created":"2018-07-31","cited_by_count":10},{"id":"2009.07025","title":"FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment","created":"2020-09-12","cited_by_count":7},{"id":"1911.08080","title":"Jointly De-biasing Face Recognition and Demographic Attribute Estimation","created":"2019-11-19","cited_by_count":6},{"id":"2003.07680","title":"Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study","created":"2020-03-13","cited_by_count":4},{"id":"2006.03985","title":"Enhancing Facial Data Diversity with Style-based Face Aging","created":"2020-06-06","cited_by_count":3}],"next_cursor":null}