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Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, R. Fergus, and P. Perona · 2004
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Clustered pose and nonlinear appearance models for human pose estimation
Sam Johnson and Mark Everingham · 2010
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Freesound technical demo
Frederic Font, Gerard Roma, and Xavier Serra · 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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Esc: Dataset for environmental sound classification
Karol J Piczak · 2015
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Image and Vision Computing , 2016
300 faces in-the-wild challenge: database and results · 2016
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A vector-contraction inequality for rademacher complexities
Andreas Maurer · 2016
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Using deep learning for image-based plant disease detection
Sharada P Mohanty, David P Hughes, and Marcel Salathé · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Neural audio synthesis of musical notes with wavenet autoencoders
Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi · 2017
Cited alongside, same era.
Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
Cited alongside, same era.
Voxceleb: a large-scale speaker identification dataset
A. Nagrani, J. S. Chung, and A. Zisserman · 2017
Cited alongside, same era.
General-purpose tagging of freesound audio with audioset labels: Task description, dataset, and baseline
Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, and Xavier Serra · 2018
Cited alongside, same era.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
Cited alongside, same era.
Hypernetworks(github)
G. Mittal · 2018
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Clar: contrastive learning of auditory representations
Haider Al-Tahan and Yalda Mohsenzadeh · 2021
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Big self-supervised models advance medical image classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, and Mohammad Norouzi · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 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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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
Cited alongside, same era.
Pytorch image models
Ross Wightman · 2019
Cited alongside, same era.
Learning invariances in neural networks
Gregory W Benton, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2020
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Cited alongside, same era.
How well do self-supervised models transfer?
Linus Ericsson, Henry Gouk, and Timothy M Hospedales · 2021
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Fsd50k: an open dataset of human-labeled sound events
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra · 2021
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Distance-based regularisation of deep networks for fine-tuning
Henry Gouk, Timothy Hospedales, et al · 2021
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Improving transferability of representations via augmentation-aware self-supervision
Hankook Lee, Kibok Lee, Kimin Lee, Honglak Lee, and Jinwoo Shin · 2021
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Advances in Neural Information Processing Systems , 2021
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, and David K Duvenaud · 2021
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Metaaudio: A few-shot audio classification benchmark
Calum Heggan, Sam Budgett, Timothy Hospedales, and Mehrdad Yaghoobi · 2022
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Invariance learning in deep neural networks with differentiable laplace approximations
Alexander Immer, Tycho FA van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, and Mark van der Wilk · 2022
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Self-supervised learning in medicine and healthcare
Rayan Krishnan, Pranav Rajpurkar, and Eric J Topol · 2022
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On the importance of hyperparameters and data augmentation for self-supervised learning
Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, and Frank Hutter · 2022
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