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There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic.
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Jerzy Neyman · 1992
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Tour the World: Building a Web-Scale Landmark Recognition Engine
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Retrieving Landmark and Non-landmark Images from Community Photo Collections
Y. Avrithis, Y. Kalantidis, G. Tolias, and E. Spyrou · 2010
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ImageNet large scale visual recognition challenge
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Visual landmark recognition from Internet photo collections: A large-scale evaluation
Weyand, T. and Leibe, B · 2015
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Large-Scale Image Retrieval with Attentive Deep Local Features
H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han · 2017
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Fair and diverse DPP-based data summarization
Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi · 2018
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The iNaturalist species classification and detection dataset
Yang Song Yin Cui Chen Sun Alex Shepard Hartwig Adam Pietro Perona Serge Belongie Grant Van Horn, Oisin Mac Aodha · 2018
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FairGAN: Fairness-aware generative adversarial networks
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2018
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A survey on bias and fairness in machine learning, 2019
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Diversity in faces, 2019
Michele Merler, Nalini Ratha, Rogerio S. Feris, and John R. Smith · 2019
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Discovering fair representations in the data domain
Novi Quadrianto, Viktoriia Sharmanska, and Oliver Thomas · 2019
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Unifying deep local and global features for image search
Bingyi Cao, Andre Araujo, and Jack Sim · 2020
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Two-stage discriminative re-ranking for large-scale landmark retrieval, 2020
Shuhei Yokoo, Kohei Ozaki, Edgar Simo-Serra, and Satoshi Iizuka · 2020
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https://www.kaggle.com/c/landmark-recognition-2021
Google landmark recognition 2021 · 2021
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https://www.kaggle.com/c/landmark-retrieval-2021
Google landmark retrieval 2021 · 2021
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Data commons place explorer
Data Commons · 2021
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Towards accountability for machine learning datasets: Practices from software engineering and infrastructure, 2021
Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell · 2021
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Class-Balanced Distillation for Long-Tailed Visual Recognition
Ahmet Iscen, Andre Araujo, Boqing Gong, and Cordelia Schmid · 2021
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Bringing the people back in: Contesting benchmark machine learning datasets, 2020
Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, and Morgan Klaus Scheuerman · 2020
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Lessons from archives: Strategies for collecting sociocultural data in machine learning
Eun Seo Jo and Timnit Gebru · 2020
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Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval
Tobias Weyand, André Araujo, Bingyi Cao, and Jack Sim · 2020
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Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
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Towards a fairer landmark recognition dataset, 2021
Zu Kim, André Araujo, Bingyi Cao, Cam Askew, Jack Sim, Mike Green, N’Mah Fodiatu Yilla, and Tobias Weyand · 2021
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Population of WORLD 2019
PopulationPyramid.net · 2021
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”Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI, 2021
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Kumar Paritosh, and Lora Mois Aroyo · 2021
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2nd place solution to google landmark retrieval 2021, 2021
Zhang Yuqi, Xu Xianzhe, Chen Weihua, Wang Yaohua, Zhang Fangyi, Wang Fan, and Li Hao · 2021
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