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diff --git a/site/datasets/verified/msceleb.csv b/site/datasets/verified/msceleb.csv index be5b063c..5cf48ab3 100644 --- a/site/datasets/verified/msceleb.csv +++ b/site/datasets/verified/msceleb.csv @@ -93,3 +93,32 @@ id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,t 91,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,c7c8d150ece08b12e3abdb6224000c07a6ce7d47,citation,https://arxiv.org/pdf/1611.05271.pdf,DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification,2018 92,South Korea,MsCeleb,msceleb,36.0138857,129.3231836,POSTECH,edu,e6b45d5a86092bbfdcd6c3c54cda3d6c3ac6b227,citation,https://arxiv.org/pdf/1808.04976.pdf,Pairwise Relational Networks for Face Recognition,2018 93,China,MsCeleb,msceleb,30.318764,120.363977,China Jiliang University,edu,406c5aeca71011fd8f8bd233744a81b53ccf635a,citation,,Scalable softmax loss for face verification,2017 +94,India,MsCeleb,msceleb,28.5456282,77.2731505,"IIIT Delhi, India",edu,c43d3ad956118ea1d26d39903097e2db86eae822,citation,https://arxiv.org/pdf/1904.01219.pdf,Deep Learning for Face Recognition: Pride or Prejudiced?,2019 +95,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +96,China,MsCeleb,msceleb,28.2290209,112.99483204,"National University of Defense Technology, China",mil,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +97,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +98,China,MsCeleb,msceleb,39.9808333,116.34101249,Beihang University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +99,China,MsCeleb,msceleb,40.00229045,116.32098908,Tsinghua University,edu,7b0ed3d67375a4542133c992f4e55fd4ade0cd90,citation,https://arxiv.org/pdf/1904.09149.pdf,Knowledge Distillation via Route Constrained Optimization,2019 +100,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +101,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +102,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018 +103,United States,MsCeleb,msceleb,33.776033,-84.39884086,Georgia Institute of Technology,edu,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +104,United States,MsCeleb,msceleb,37.3706254,-121.9671894,NVIDIA,company,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +105,China,MsCeleb,msceleb,23.0502042,113.39880323,South China University of Technology,edu,5b7a5b8ea99ea79e0a0ae53b45bc9b2b1aa99952,citation,https://arxiv.org/pdf/1805.09298.pdf,Learning towards Minimum Hyperspherical Energy,2018 +106,Singapore,MsCeleb,msceleb,1.3484104,103.68297965,Nanyang Technological University,edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +107,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +108,United States,MsCeleb,msceleb,32.87935255,-117.23110049,"University of California, San Diego",edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +109,China,MsCeleb,msceleb,22.4162632,114.2109318,Chinese University of Hong Kong,edu,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +110,China,MsCeleb,msceleb,39.993008,116.329882,SenseTime,company,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018 +111,United States,MsCeleb,msceleb,45.57022705,-122.63709346,Concordia University,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +112,United States,MsCeleb,msceleb,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +113,United States,MsCeleb,msceleb,36.0678324,-94.1736551,University of Arkansas,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018 +114,United States,MsCeleb,msceleb,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,https://arxiv.org/pdf/1607.08221.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 +115,China,MsCeleb,msceleb,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3c5ba48d25fbe24691ed060fa8f2099cc9eba14f,citation,https://arxiv.org/pdf/1812.00194.pdf,Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation,2018 +116,China,MsCeleb,msceleb,22.4162632,114.2109318,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 +117,Spain,MsCeleb,msceleb,40.4167754,-3.7037902,"Computer Vision Group (www.vision4uav.com), Centro de Automática y Robótica (CAR) UPM-CSIC, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006, Spain",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +118,India,MsCeleb,msceleb,13.5568171,80.0261283,"Indian Institute of Information Technology, Sri City, India",edu,726f76f11e904d7fcb12736c276a0b00eb5cde49,citation,https://arxiv.org/pdf/1901.05903.pdf,A Performance Comparison of Loss Functions for Deep Face Recognition,2019 +119,China,MsCeleb,msceleb,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,3a27d164e931c422d16481916a2fa6401b74bcef,citation,https://arxiv.org/pdf/1709.03654.pdf,Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification,2018 +120,China,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 +121,China,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 +122,China,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 |