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index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,MsCeleb,msceleb,0.0,0.0,,,291265db88023e92bb8c8e6390438e5da148e8f5,main,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016
1,MsCeleb,msceleb,32.0565957,118.77408833,Nanjing University,edu,e47e8fa44decf9adbcdb02f8a64b802fe33b29ef,citation,https://doi.org/10.1109/TIP.2017.2782366,Robust Distance Metric Learning via Bayesian Inference,2018
2,MsCeleb,msceleb,48.8476037,2.2639934,"Université Paris-Saclay, France",edu,96e318f8ff91ba0b10348d4de4cb7c2142eb8ba9,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364450,State-of-the-art face recognition performance using publicly available software and datasets,2018
3,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0a0321785c8beac1cbaaec4d8ad0cfd4a0d6d457,citation,https://pdfs.semanticscholar.org/0a03/21785c8beac1cbaaec4d8ad0cfd4a0d6d457.pdf,Learning Invariant Deep Representation for NIR-VIS Face Recognition,2017
4,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7a131fafa7058fb75fdca32d0529bc7cb50429bd,citation,https://arxiv.org/pdf/1704.04086.pdf,Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis,2017
5,MsCeleb,msceleb,30.40550035,-91.18620474,Louisiana State University,edu,9f65319b8a33c8ec11da2f034731d928bf92e29d,citation,http://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf,Taking Roll: a Pipeline for Face Recognition,2018
6,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,809ea255d144cff780300440d0f22c96e98abd53,citation,http://pdfs.semanticscholar.org/809e/a255d144cff780300440d0f22c96e98abd53.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018
7,MsCeleb,msceleb,31.28473925,121.49694909,Tongji University,edu,fe0cf8eaa5a5f59225197ef1bb8613e603cd96d4,citation,https://pdfs.semanticscholar.org/4e20/8cfff33327863b5aeef0bf9b327798a5610c.pdf,Improved Face Verification with Simple Weighted Feature Combination,2017
8,MsCeleb,msceleb,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017
9,MsCeleb,msceleb,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017
10,MsCeleb,msceleb,31.83907195,117.26420748,University of Science and Technology of China,edu,e1256ff535bf4c024dd62faeb2418d48674ddfa2,citation,https://arxiv.org/pdf/1803.11182.pdf,Towards Open-Set Identity Preserving Face Synthesis,2018
11,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,5812d8239d691e99d4108396f8c26ec0619767a6,citation,https://arxiv.org/pdf/1810.09951.pdf,GhostVLAD for set-based face recognition,2018
12,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0b8b8776684009e537b9e2c0d87dbd56708ddcb4,citation,http://pdfs.semanticscholar.org/0b8b/8776684009e537b9e2c0d87dbd56708ddcb4.pdf,Adversarial Discriminative Heterogeneous Face Recognition,2017
13,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018
14,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition,2018
15,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,212608e00fc1e8912ff845ee7a4a67f88ba938fc,citation,https://arxiv.org/pdf/1704.02450.pdf,Coupled Deep Learning for Heterogeneous Face Recognition,2018
16,MsCeleb,msceleb,45.7413921,126.62552755,Harbin Institute of Technology,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018
17,MsCeleb,msceleb,38.0333742,-84.5017758,University of Kentucky,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018
18,MsCeleb,msceleb,34.0224149,-118.28634407,University of Southern California,edu,b73795963dc623a634d218d29e4a5b74dfbc79f1,citation,https://arxiv.org/pdf/1807.08772.pdf,Identity Preserving Face Completion for Large Ocular Region Occlusion,2018
19,MsCeleb,msceleb,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,102280e80470ace006e14d6ec9adda082603dea1,citation,https://arxiv.org/pdf/1804.04418.pdf,Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector,2018
20,MsCeleb,msceleb,55.94951105,-3.19534913,University of Edinburgh,edu,102280e80470ace006e14d6ec9adda082603dea1,citation,https://arxiv.org/pdf/1804.04418.pdf,Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector,2018
21,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,bd379f8e08f88729a9214260e05967f4ca66cd65,citation,https://arxiv.org/pdf/1711.06148.pdf,Learning Compositional Visual Concepts with Mutual Consistency,2017
22,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018
23,MsCeleb,msceleb,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018
24,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,http://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,Gan Quality Index (gqi) by Gan-induced Classifier,2018
25,MsCeleb,msceleb,40.8722825,-73.89489171,City University of New York,edu,f74917fc0e55f4f5682909dcf6929abd19d33e2e,citation,http://pdfs.semanticscholar.org/f749/17fc0e55f4f5682909dcf6929abd19d33e2e.pdf,Gan Quality Index (gqi) by Gan-induced Classifier,2018
26,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017
27,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018
28,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017
29,MsCeleb,msceleb,23.09461185,113.28788994,Sun Yat-Sen University,edu,44f48a4b1ef94a9104d063e53bf88a69ff0f55f3,citation,http://pdfs.semanticscholar.org/44f4/8a4b1ef94a9104d063e53bf88a69ff0f55f3.pdf,Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models,2016
30,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018
31,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2011d4da646f794456bebb617d1500ddf71989ed,citation,https://pdfs.semanticscholar.org/2011/d4da646f794456bebb617d1500ddf71989ed.pdf,Transductive Centroid Projection for Semi-supervised Large-Scale Recognition,2018
32,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,9e182e0cd9d70f876f1be7652c69373bcdf37fb4,citation,https://arxiv.org/pdf/1807.07860.pdf,Talking Face Generation by Adversarially Disentangled Audio-Visual Representation,2018
33,MsCeleb,msceleb,49.2767454,-122.91777375,Simon Fraser University,edu,e8ef22b6da1dd3a4e014b96e6073a7b610fd97ea,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018
34,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,e8ef22b6da1dd3a4e014b96e6073a7b610fd97ea,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018
35,MsCeleb,msceleb,39.977217,116.337632,Microsoft Research Asia,company,e8ef22b6da1dd3a4e014b96e6073a7b610fd97ea,citation,https://arxiv.org/pdf/1803.06340.pdf,Faces as Lighting Probes via Unsupervised Deep Highlight Extraction,2018
36,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017
37,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018
38,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018
39,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,8e0ab1b08964393e4f9f42ca037220fe98aad7ac,citation,https://arxiv.org/pdf/1712.04695.pdf,UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition,2017
40,MsCeleb,msceleb,41.10427915,29.02231159,Istanbul Technical University,edu,361eaef45fccfffd5b7df12fba902490a7d24a8d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404319,Robust deep learning features for face recognition under mismatched conditions,2018
41,MsCeleb,msceleb,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,45b9b7fe3850ef83d39d52f6edcc0c24fcc0bc73,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7888593,Learning Neural Bag-of-Features for Large-Scale Image Retrieval,2017
42,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0
43,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,c71b0ed402437470f229b3fdabb88ad044c092ea,citation,https://pdfs.semanticscholar.org/c71b/0ed402437470f229b3fdabb88ad044c092ea.pdf,Dynamic Conditional Networks for Few-Shot Learning,2018
44,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,c71b0ed402437470f229b3fdabb88ad044c092ea,citation,https://pdfs.semanticscholar.org/c71b/0ed402437470f229b3fdabb88ad044c092ea.pdf,Dynamic Conditional Networks for Few-Shot Learning,2018
45,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,99daa2839213f904e279aec7cef26c1dfb768c43,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018
46,MsCeleb,msceleb,31.30104395,121.50045497,Fudan University,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via Active Annotation and Learning,2016
47,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via Active Annotation and Learning,2016
48,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,0a64f4fec592662316764283575d05913eb2135b,citation,http://pdfs.semanticscholar.org/0a64/f4fec592662316764283575d05913eb2135b.pdf,Joint Pixel and Feature-level Domain Adaptation in the Wild,2018
49,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,c71217b2b111a51a31cf1107c71d250348d1ff68,citation,https://arxiv.org/pdf/1703.09912.pdf,One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models,2017
50,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,de7f5e4ccc2f38e0c8f3f72a930ae1c43e0fdcf0,citation,https://arxiv.org/pdf/1707.03986.pdf,Merge or Not? Learning to Group Faces via Imitation Learning,2018
51,MsCeleb,msceleb,40.47913175,-74.43168868,Rutgers University,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0
52,MsCeleb,msceleb,33.9928298,-81.02685168,University of South Carolina,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0
53,MsCeleb,msceleb,39.94976005,116.33629046,Beijing Jiaotong University,edu,d7cbedbee06293e78661335c7dd9059c70143a28,citation,https://arxiv.org/pdf/1804.07573.pdf,MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices,2018
54,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,40bb090a4e303f11168dce33ed992f51afe02ff7,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf,Marginal Loss for Deep Face Recognition,2017
55,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,http://pdfs.semanticscholar.org/38d8/ff137ff753f04689e6b76119a44588e143f3.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017
56,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,c9efcd8e32dced6efa2bba64789df8d0a8e4996a,citation,http://dl.acm.org/citation.cfm?id=2984060,Deep Convolutional Neural Network with Independent Softmax for Large Scale Face Recognition,2016
57,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,43fe03ec1acb6ea9d05d2b22eeddb2631bd30437,citation,https://doi.org/10.1109/ICIP.2017.8296394,Weakly supervised multiscale-inception learning for web-scale face recognition,2017
58,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0
59,MsCeleb,msceleb,40.0044795,116.370238,Chinese Academy of Sciences,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0
60,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0
61,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,a322479a6851f57a3d74d017a9cb6d71395ed806,citation,https://pdfs.semanticscholar.org/a322/479a6851f57a3d74d017a9cb6d71395ed806.pdf,Towards Pose Invariant Face Recognition in the Wild,0
62,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d949fadc9b6c5c8b067fa42265ad30945f9caa99,citation,https://arxiv.org/pdf/1710.00870.pdf,Rethinking Feature Discrimination and Polymerization for Large-scale Recognition,2017
63,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6fed504da4e192fe4c2d452754d23d3db4a4e5e3,citation,http://pdfs.semanticscholar.org/85ee/d639f7367c794a6d8b38619697af3efaacfe.pdf,Learning Deep Features via Congenerous Cosine Loss for Person Recognition,2017
64,MsCeleb,msceleb,39.9586652,116.30971281,Beijing Institute of Technology,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
65,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
66,MsCeleb,msceleb,1.2962018,103.77689944,National University of Singapore,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
67,MsCeleb,msceleb,39.65404635,-79.96475355,West Virginia University,edu,f1245d318eb3d775e101355f5f085a9bc4a0339b,citation,https://pdfs.semanticscholar.org/f124/5d318eb3d775e101355f5f085a9bc4a0339b.pdf,Face Verification with Disguise Variations via Deep Disguise,0
68,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018
69,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,ad2cb5c255e555d9767d526721a4c7053fa2ac58,citation,https://arxiv.org/pdf/1711.03990.pdf,Longitudinal Study of Child Face Recognition,2018
70,MsCeleb,msceleb,50.7791703,6.06728733,RWTH Aachen University,edu,f02f0f6fcd56a9b1407045de6634df15c60a85cd,citation,http://pdfs.semanticscholar.org/f02f/0f6fcd56a9b1407045de6634df15c60a85cd.pdf,Learning Low-shot facial representations via 2D warping,2017
71,MsCeleb,msceleb,25.01682835,121.53846924,National Taiwan University,edu,17423fe480b109e1d924314c1dddb11b084e8a42,citation,https://pdfs.semanticscholar.org/1742/3fe480b109e1d924314c1dddb11b084e8a42.pdf,Deep Disguised Faces Recognition,0
72,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,d29eec5e047560627c16803029d2eb8a4e61da75,citation,http://pdfs.semanticscholar.org/d29e/ec5e047560627c16803029d2eb8a4e61da75.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018
73,MsCeleb,msceleb,51.49887085,-0.17560797,Imperial College London,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018
74,MsCeleb,msceleb,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018
75,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,44e6ce12b857aeade03a6e5d1b7fb81202c39489,citation,https://arxiv.org/pdf/1806.05622.pdf,VoxCeleb2: Deep Speaker Recognition,2018
76,MsCeleb,msceleb,23.0502042,113.39880323,South China University of Technology,edu,4f10a7697fb2a2c626d1190db2afba83c4ffe856,citation,https://pdfs.semanticscholar.org/4f10/a7697fb2a2c626d1190db2afba83c4ffe856.pdf,Cartoon-to-Photo Facial Translation with Generative Adversarial Networks,2018
77,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,44b827df6c433ca49bcf44f9f3ebfdc0774ee952,citation,https://doi.org/10.1109/LSP.2017.2726105,Deep Correlation Feature Learning for Face Verification in the Wild,2017
78,MsCeleb,msceleb,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,e7b2b0538731adaacb2255235e0a07d5ccf09189,citation,https://arxiv.org/pdf/1803.10837.pdf,Learning Deep Representations with Probabilistic Knowledge Transfer,2018
79,MsCeleb,msceleb,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,46702e0127e16a4d6a1feda3ffc5f0f123957e87,citation,https://arxiv.org/pdf/1809.06131.pdf,Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition,2018
80,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,eb027969f9310e0ae941e2adee2d42cdf07d938c,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018
81,MsCeleb,msceleb,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017
82,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018
83,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018
84,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018
85,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,39c10888a470b92b917788c57a6fd154c97b421c,citation,https://doi.org/10.1109/VCIP.2017.8305036,Joint multi-feature fusion and attribute relationships for facial attribute prediction,2017
86,MsCeleb,msceleb,41.70456775,-86.23822026,University of Notre Dame,edu,987a649cb33302c41412419f8eeb77048aa5513e,citation,https://arxiv.org/pdf/1803.07140.pdf,Visual Psychophysics for Making Face Recognition Algorithms More Explainable,2018
87,MsCeleb,msceleb,42.36782045,-71.12666653,Harvard University,edu,987a649cb33302c41412419f8eeb77048aa5513e,citation,https://arxiv.org/pdf/1803.07140.pdf,Visual Psychophysics for Making Face Recognition Algorithms More Explainable,2018
88,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0
89,MsCeleb,msceleb,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018
90,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,f61d5f2a082c65d5330f21b6f36312cc4fab8a3b,citation,https://arxiv.org/pdf/1705.08841.pdf,Multi-Level Variational Autoencoder: Learning Disentangled Representations From Grouped Observations,2018
91,MsCeleb,msceleb,42.4505507,-76.4783513,Cornell University,edu,dec0c26855da90876c405e9fd42830c3051c2f5f,citation,https://pdfs.semanticscholar.org/dec0/c26855da90876c405e9fd42830c3051c2f5f.pdf,Supplementary Material : Learning Compositional Visual Concepts with Mutual Consistency,2018
92,MsCeleb,msceleb,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018
93,MsCeleb,msceleb,39.329053,-76.619425,Johns Hopkins University,edu,01dfd60c0851c4e5a99176e99aa369e1b5f606b7,citation,https://arxiv.org/pdf/1809.01936.pdf,Disentangled Variational Representation for Heterogeneous Face Recognition,2018
94,MsCeleb,msceleb,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018
95,MsCeleb,msceleb,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018
96,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
97,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
98,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017
99,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
100,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
101,MsCeleb,msceleb,37.4102193,-122.05965487,Carnegie Mellon University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017
102,MsCeleb,msceleb,37.43131385,-122.16936535,Stanford University,edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017
103,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,d35534f3f59631951011539da2fe83f2844ca245,citation,https://arxiv.org/pdf/1705.07904.pdf,Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,2017
104,MsCeleb,msceleb,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017
105,MsCeleb,msceleb,51.7534538,-1.25400997,University of Oxford,edu,313d5eba97fe064bdc1f00b7587a4b3543ef712a,citation,https://pdfs.semanticscholar.org/cb7f/93467b0ec1afd43d995e511f5d7bf052a5af.pdf,Compact Deep Aggregation for Set Retrieval,2018
106,MsCeleb,msceleb,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017
107,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018
108,MsCeleb,msceleb,22.42031295,114.20788644,Chinese University of Hong Kong,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018
109,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,831b4d8b0c0173b0bac0e328e844a0fbafae6639,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018
110,MsCeleb,msceleb,29.7207902,-95.34406271,University of Houston,edu,3cb2841302af1fb9656f144abc79d4f3d0b27380,citation,https://pdfs.semanticscholar.org/3cb2/841302af1fb9656f144abc79d4f3d0b27380.pdf,When 3 D-Aided 2 D Face Recognition Meets Deep Learning : An extended UR 2 D for Pose-Invariant Face Recognition,2017
111,MsCeleb,msceleb,39.2899685,-76.62196103,University of Maryland,edu,23dd8d17ce09c22d367e4d62c1ccf507bcbc64da,citation,https://pdfs.semanticscholar.org/23dd/8d17ce09c22d367e4d62c1ccf507bcbc64da.pdf,Deep Density Clustering of Unconstrained Faces ( Supplementary Material ),2018
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