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index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,IMDB,imdb_wiki,0.0,0.0,,,10195a163ab6348eef37213a46f60a3d87f289c5,main,https://doi.org/10.1007/s11263-016-0940-3,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016
1,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017
2,IMDB,imdb_wiki,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017
3,IMDB,imdb_wiki,51.5231607,-0.1282037,University College London,edu,3c4f6d24b55b1fd3c5b85c70308d544faef3f69a,citation,http://pdfs.semanticscholar.org/3c4f/6d24b55b1fd3c5b85c70308d544faef3f69a.pdf,A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics,2017
4,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,13719bbb4bb8bbe0cbcdad009243a926d93be433,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Tian_Deep_LDA-Pruned_Nets_CVPR_2017_paper.pdf,Deep LDA-Pruned Nets for Efficient Facial Gender Classification,2017
5,IMDB,imdb_wiki,41.3868913,2.16352385,University of Barcelona,edu,b7845e0b0ce17cde7db37d5524ef2a61dee3e540,citation,https://doi.org/10.1109/ICPR.2016.7899608,Fusion of classifier predictions for audio-visual emotion recognition,2016
6,IMDB,imdb_wiki,44.812384,20.453501,Singidunum University,edu,b7845e0b0ce17cde7db37d5524ef2a61dee3e540,citation,https://doi.org/10.1109/ICPR.2016.7899608,Fusion of classifier predictions for audio-visual emotion recognition,2016
7,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,b7845e0b0ce17cde7db37d5524ef2a61dee3e540,citation,https://doi.org/10.1109/ICPR.2016.7899608,Fusion of classifier predictions for audio-visual emotion recognition,2016
8,IMDB,imdb_wiki,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
9,IMDB,imdb_wiki,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
10,IMDB,imdb_wiki,55.91029135,-3.32345777,Heriot-Watt University,edu,2cdc40f20b70ca44d9fd8e7716080ee05ca7924a,citation,http://pdfs.semanticscholar.org/2cdc/40f20b70ca44d9fd8e7716080ee05ca7924a.pdf,Real-time Convolutional Neural Networks for Emotion and Gender Classification,2017
11,IMDB,imdb_wiki,56.45796755,-2.98214831,University of Dundee,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017
12,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,d5b0e73b584be507198b6665bcddeba92b62e1e5,citation,http://pdfs.semanticscholar.org/d5b0/e73b584be507198b6665bcddeba92b62e1e5.pdf,Multi-Region Ensemble Convolutional Neural Networks for High-Accuracy Age Estimation,2017
13,IMDB,imdb_wiki,24.12084345,120.67571165,National Chung Hsing University,edu,6feafc5c1d8b0e9d65ebe4c1512b7860c538fbdc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8448885,Smart Facial Age Estimation with Stacked Deep Network Fusion,2018
14,IMDB,imdb_wiki,24.15031065,120.68325501,National Taichung University of Science and Technology,edu,6feafc5c1d8b0e9d65ebe4c1512b7860c538fbdc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8448885,Smart Facial Age Estimation with Stacked Deep Network Fusion,2018
15,IMDB,imdb_wiki,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016
16,IMDB,imdb_wiki,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018
17,IMDB,imdb_wiki,45.47567215,9.23336232,Università degli Studi di Milano,edu,a713a01971e73d0c3118d0409dc7699a24f521d6,citation,https://doi.org/10.1109/SSCI.2017.8285381,Age estimation based on face images and pre-trained convolutional neural networks,2017
18,IMDB,imdb_wiki,35.6894875,139.6917064,"IBJ, Inc., Tokyo, Japan",company,df7af280771a6c8302b75ed0a14ffe7854cca679,citation,http://doi.ieeecomputersociety.org/10.1109/ICMEW.2017.8026293,Prediction of users' facial attractiveness on an online dating website,2017
19,IMDB,imdb_wiki,35.9020448,139.93622009,University of Tokyo,edu,df7af280771a6c8302b75ed0a14ffe7854cca679,citation,http://doi.ieeecomputersociety.org/10.1109/ICMEW.2017.8026293,Prediction of users' facial attractiveness on an online dating website,2017
20,IMDB,imdb_wiki,35.9990522,-78.9290629,Duke University,edu,cca9ae621e8228cfa787ec7954bb375536160e0d,citation,https://arxiv.org/pdf/1805.07410.pdf,Learning to Collaborate for User-Controlled Privacy,2018
21,IMDB,imdb_wiki,51.5231607,-0.1282037,University College London,edu,cca9ae621e8228cfa787ec7954bb375536160e0d,citation,https://arxiv.org/pdf/1805.07410.pdf,Learning to Collaborate for User-Controlled Privacy,2018
22,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,3ca5d3b8f5f071148cb50f22955fd8c1c1992719,citation,http://pdfs.semanticscholar.org/3ca5/d3b8f5f071148cb50f22955fd8c1c1992719.pdf,Evaluating race and sex diversity in the world's largest companies using deep neural networks,2017
23,IMDB,imdb_wiki,53.57227,9.99472,"Dermalog Identification Systems, Hamburg, Germany",company,5b64584d6b01e66dfd0b6025b2552db1447ccdeb,citation,https://doi.org/10.1109/BTAS.2017.8272697,Deep expectation for estimation of fingerprint orientation fields,2017
24,IMDB,imdb_wiki,60.7897318,10.6821927,"Norwegian Biometrics Lab, NTNU, Norway",edu,5b64584d6b01e66dfd0b6025b2552db1447ccdeb,citation,https://doi.org/10.1109/BTAS.2017.8272697,Deep expectation for estimation of fingerprint orientation fields,2017
25,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018
26,IMDB,imdb_wiki,51.5231607,-0.1282037,University College London,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018
27,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,56e079f4eb40744728fd1d7665938b06426338e5,citation,https://arxiv.org/pdf/1705.04293.pdf,Bayesian Approaches to Distribution Regression,2018
28,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,407bb798ab153bf6156ba2956f8cf93256b6910a,citation,http://pdfs.semanticscholar.org/407b/b798ab153bf6156ba2956f8cf93256b6910a.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018
29,IMDB,imdb_wiki,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
30,IMDB,imdb_wiki,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
31,IMDB,imdb_wiki,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017
32,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017
33,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0
34,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0
35,IMDB,imdb_wiki,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016
36,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016
37,IMDB,imdb_wiki,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018
38,IMDB,imdb_wiki,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018
39,IMDB,imdb_wiki,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018
40,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,5f94969b9491db552ffebc5911a45def99026afe,citation,https://pdfs.semanticscholar.org/5f94/969b9491db552ffebc5911a45def99026afe.pdf,Multimodal Learning and Reasoning for Visual Question Answering,2017
41,IMDB,imdb_wiki,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017
42,IMDB,imdb_wiki,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018
43,IMDB,imdb_wiki,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018
44,IMDB,imdb_wiki,43.66333345,-79.39769975,University of Toronto,edu,36a3a96ef54000a0cd63de867a5eb7e84396de09,citation,http://www.cs.toronto.edu/~guerzhoy/oriviz/crv17.pdf,Automatic Photo Orientation Detection with Convolutional Neural Networks,2017
45,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018
46,IMDB,imdb_wiki,51.5247272,-0.03931035,Queen Mary University of London,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017
47,IMDB,imdb_wiki,35.7036227,51.35125097,Sharif University of Technology,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017
48,IMDB,imdb_wiki,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018
49,IMDB,imdb_wiki,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018
50,IMDB,imdb_wiki,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018
51,IMDB,imdb_wiki,35.9042272,-78.85565763,"IBM Research, North Carolina",company,00a967cb2d18e1394226ad37930524a31351f6cf,citation,https://arxiv.org/pdf/1611.05377v1.pdf,Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification,2017
52,IMDB,imdb_wiki,12.9803537,77.6975101,"Samsung R&D Institute, Bangalore, India",company,cf736f596bf881ca97ec4b29776baaa493b9d50e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952629,Low Dimensional Deep Features for facial landmark alignment,2017
53,IMDB,imdb_wiki,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018
54,IMDB,imdb_wiki,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018
55,IMDB,imdb_wiki,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017
56,IMDB,imdb_wiki,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017
57,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,7f21a7441c6ded38008c1fd0b91bdd54425d3f80,citation,https://arxiv.org/pdf/1809.05474.pdf,Real Time System for Facial Analysis,2018
58,IMDB,imdb_wiki,55.94951105,-3.19534913,University of Edinburgh,edu,f5fae7810a33ed67852ad6a3e0144cb278b24b41,citation,https://pdfs.semanticscholar.org/f5fa/e7810a33ed67852ad6a3e0144cb278b24b41.pdf,Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018,2018
59,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017
60,IMDB,imdb_wiki,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
61,IMDB,imdb_wiki,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
62,IMDB,imdb_wiki,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
63,IMDB,imdb_wiki,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
64,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017
65,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017
66,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018
67,IMDB,imdb_wiki,47.6543238,-122.30800894,University of Washington,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018
68,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b8c08c1330779283b3fbf06d133faf8bd55ea941,citation,https://arxiv.org/pdf/1803.11521.pdf,Online Regression with Feature Selection in Stochastic Data Streams,2018
69,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,1cfca6b71b0ead87bbb79a8614ddec3a10100faa,citation,https://arxiv.org/pdf/1809.05465.pdf,Are screening methods useful in feature selection? An empirical study,2018
70,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017
71,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017
72,IMDB,imdb_wiki,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
73,IMDB,imdb_wiki,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
74,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0
75,IMDB,imdb_wiki,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0
76,IMDB,imdb_wiki,35.9542493,-83.9307395,University of Tennessee,edu,7fab17ef7e25626643f1d55257a3e13348e435bd,citation,https://arxiv.org/pdf/1702.08423.pdf,Age Progression/Regression by Conditional Adversarial Autoencoder,2017
77,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018
78,IMDB,imdb_wiki,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018
79,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018
80,IMDB,imdb_wiki,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016
81,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016
82,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,df31e9c882dfb3ea5a3abe3b139ceacb1d90a302,citation,https://arxiv.org/pdf/1808.09211.pdf,DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model,2018
83,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,523854a7d8755e944bd50217c14481fe1329a969,citation,https://arxiv.org/pdf/1808.00380.pdf,A Differentially Private Kernel Two-Sample Test,2018
84,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018
85,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018
86,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017
87,IMDB,imdb_wiki,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017
88,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017
89,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016
90,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016
91,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016
92,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,1648cf24c042122af2f429641ba9599a2187d605,citation,https://doi.org/10.1109/BTAS.2017.8272698,Boosting cross-age face verification via generative age normalization,2017
93,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,ca37933b6297cdca211aa7250cbe6b59f8be40e5,citation,http://doi.acm.org/10.1145/3155133.3155207,"Multi-task learning for smile detection, emotion recognition and gender classification",2017
94,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,cf2002fac81ccdccdadb5cc43f7b1cd30882d2c2,citation,https://arxiv.org/pdf/1803.09546.pdf,Calibrated Prediction Intervals for Neural Network Regressors,2018
95,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,75f9d3533f175943e33c9155f4038488f32a24bc,citation,https://arxiv.org/pdf/1811.06817.pdf,Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control,2018
96,IMDB,imdb_wiki,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
97,IMDB,imdb_wiki,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
98,IMDB,imdb_wiki,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
99,IMDB,imdb_wiki,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018
100,IMDB,imdb_wiki,42.0551164,-87.67581113,Northwestern University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018
101,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018
102,IMDB,imdb_wiki,30.04287695,31.23664139,American University in Cairo,edu,3a2c90e0963bfb07fc7cd1b5061383e9a99c39d2,citation,https://arxiv.org/pdf/1710.03804.pdf,End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,2017
103,IMDB,imdb_wiki,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017
104,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,1491d0938bb4183bd19f2fee3b61997e1918160d,citation,https://arxiv.org/pdf/1807.00453.pdf,Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision,2018
105,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b88bace97d214d279e3a2053ccff0b6425295708,citation,https://arxiv.org/pdf/1803.11521.pdf,A Novel Framework for Online Supervised Learning with Feature Selection,2018
106,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,b20cfbb2348984b4e25b6b9174f3c7b65b6aed9e,citation,http://pdfs.semanticscholar.org/b20c/fbb2348984b4e25b6b9174f3c7b65b6aed9e.pdf,Learning with Ambiguous Label Distribution for Apparent Age Estimation,2016
107,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,f3ec43a7b22f6e5414fec473acda8ffd843e7baf,citation,https://arxiv.org/pdf/1809.07447.pdf,A Coupled Evolutionary Network for Age Estimation,2018
108,IMDB,imdb_wiki,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017
109,IMDB,imdb_wiki,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017
110,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017
111,IMDB,imdb_wiki,31.846918,117.29053367,Hefei University of Technology,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018
112,IMDB,imdb_wiki,1.3484104,103.68297965,Nanyang Technological University,edu,dc9c0527f8d4461b1742cccc7317fec8dd96d81a,citation,https://arxiv.org/pdf/1805.08373.pdf,Speeding-Up Age Estimation in Intelligent Demographics System via Network Optimization,2018
113,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,f7b422df567ce9813926461251517761e3e6cda0,citation,https://arxiv.org/pdf/1702.01983.pdf,Face aging with conditional generative adversarial networks,2017
114,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,68573e296f069071d071fc158e974e8bc70c893f,citation,https://pdfs.semanticscholar.org/6857/3e296f069071d071fc158e974e8bc70c893f.pdf,"Effective Deep Multi-source Multi-task Learning Frameworks for Smile Detection, Emotion Recognition and Gender Classification",2018
115,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017
116,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017
117,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016
118,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,f519723238701849f1160d5a9cedebd31017da89,citation,http://pdfs.semanticscholar.org/f519/723238701849f1160d5a9cedebd31017da89.pdf,Impact of multi-focused images on recognition of soft biometric traits,2016
119,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,4eab317b5ac436a949849ed286baa3de2a541eef,citation,https://arxiv.org/pdf/1809.02169.pdf,Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings,2018
120,IMDB,imdb_wiki,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
121,IMDB,imdb_wiki,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
122,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,70c59dc3470ae867016f6ab0e008ac8ba03774a1,citation,https://arxiv.org/pdf/1710.08092.pdf,VGGFace2: A Dataset for Recognising Faces across Pose and Age,2018
123,IMDB,imdb_wiki,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017
124,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,bc99ff149c3c75b90f0110b8e72a9ae1300e29e7,citation,https://arxiv.org/pdf/1804.02744.pdf,Unsupervised Learning of Mixture Models with a Uniform Background Component,2018
125,IMDB,imdb_wiki,60.18558755,24.8242733,Aalto University,edu,08d41d2f68a2bf0091dc373573ca379de9b16385,citation,https://arxiv.org/pdf/1802.05023.pdf,Recursive Chaining of Reversible Image-to-image Translators For Face Aging,2018
126,IMDB,imdb_wiki,25.0410728,121.6147562,Institute of Information Science,edu,0951f42abbf649bb564a21d4ff5dddf9a5ea54d9,citation,https://arxiv.org/pdf/1806.02023.pdf,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018
127,IMDB,imdb_wiki,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,http://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018
128,IMDB,imdb_wiki,22.5447154,113.9357164,Tencent,company,7a7fddb3020e0c2dd4e3fe275329eb10f1cfbb8a,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018
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