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Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models.
Autoencoders, minimum description length and Helmholtz free energy
Geoffrey E. Hinton and Richard S. Zemel · 1993
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Unsupervised feature learning by cross-level discrimination between instances and groups
Xudong Wang, Ziwei Liu, and Stella X Yu · 2008
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The PASCAL Visual Object Classes Challenge 2012 (VOC2011) Results
M. Everingham, L. van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Online bag-of-visual-words generation for unsupervised representation learning
Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, and Patrick Pérez · 2012
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Zenodo, 2013
European Organization For Nuclear Research and OpenAIRE · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Learning deep features for scene recognition using places database
Bolei Zhou, Àgata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
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Truth is a lie: Crowd truth and the seven myths of human annotation
Lora Aroyo and Chris Welty · 2015
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VGG image annotator (VIA)
A. Dutta, A. Gupta, and A. Zissermann · 2016
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Andreas Veit, Sami Abu-El-Haija, Serge Belongie, David Cai, Zheyun Feng, Vittorio Ferrari, Victor Gomes, Abhinav Gupta, Dhyanesh Narayanan, Chen Sun, Gal Chechik, and Kevin Murphy · 2016
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Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels
Ishan Misra, C Lawrence Zitnick, Margaret Mitchell, and Ross Girshick · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Stereotyping and bias in the flickr30k dataset
Emiel van Miltenburg · 2016
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Places: An image database for deep scene understanding
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Antonio Torralba, and Aude Oliva · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Webvision database: Visual learning and understanding from web data, 2017
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world, 2017
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D. Sculley · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Densepose: Dense human pose estimation in the wild
Rıza Alp Güler, Natalia Neverova, and Iasonas Kokkinos · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Lines of sight
Alex Hanna, Emily Denton, Razvan Amironesei, Andrew Smart, and Hilary Nicole · 2020
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Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
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The open images dataset v4
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al · 2020
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Nsfw detection machine learning model
Gant Laborde · 2020
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Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
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Between subjectivity and imposition: Power dynamics in data annotation for computer vision
Milagros Miceli, Martin Schuessler, and Tianling Yang · 2020
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Pierre Stock and Moustapha Cisse · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, X Yu Stella, and Dahua Lin · 2018
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Taskonomy: Disentangling task transfer learning
Amir Roshan Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
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Large-scale interactive object segmentation with human annotators
Rodrigo Benenson, Stefan Popov, and Vittorio Ferrari · 2019
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Excavating ai: The politics of training sets for machine learning
Kate Crawford and Trevor Paglen · 2019
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Does object recognition work for everyone?
Terrance de Vries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Data and its (dis)contents: A survey of dataset development and use in machine learning research, 2020
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, and Alex Hanna · 2020
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Concept generalization in visual representation learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus, and Karteek Alahari · 2020
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Un-mix: Rethinking image mixtures for unsupervised visual representation learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, and Eric Xing · 2020
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From ImageNet to image classification: Contextualizing progress on benchmarks
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 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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Self-supervised learning for large-scale unsupervised image clustering, 2020
Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, and Avi Mendelson · 2020
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Eqco: Equivalent rules for self-supervised contrastive learning
Benjin Zhu, Junqiang Huang, Zeming Li, Xiangyu Zhang, and Jian Sun · 2020
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Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Find identical image (FII)
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With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
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How Well Do Self-Supervised Models Transfer?
Linus Ericsson, Henry Gouk, and Timothy M. Hospedales · 2021
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Mean shift for self-supervised learning, 2021
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Contrasting contrastive self-supervised representation learning models, 2021
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Prototypical contrastive learning of unsupervised representations
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A study of “a study of face obfuscation in imagenet”
Vinay Prabhu · 2021
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Self-supervised pretraining improves self-supervised pretraining
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Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan · 2021
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Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
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A study of face obfuscation in imagenet, 2021
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What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau, and Stephen Lin · 2021
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