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We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer. 2019 · 1901
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz. 2019 · 1903
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wav2vec: Unsupervised pre-training for speech recognition
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli. 2019 · 1904
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Unsupervised data augmentation
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. 2019 · 1904
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Data-efficient image recognition with contrastive predictive coding
Olivier J. Hénaff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aäron van den Oord. 2019 · 1905
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s 4 l s^{4}l : Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer. 2019 · 1905
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006 · 2006
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2007 · 2007
Earlier work this paper cites.
Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y Ng. 2007 · 2007
Earlier work this paper cites.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al. 2014 · 2014
Earlier work this paper cites.
Image completion using planar structure guidance
Jia-Bin Huang, Sing Bing Kang, Narendra Ahuja, and Johannes Kopf. 2014 · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros. 2015 · 2015
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. 2016 · 2016
Cited alongside, same era.
Globally and locally consistent image completion
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2017 · 2017
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Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. 2018 · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018 · 2018
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Learning deep representations by mutual information estimation and maximization
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila. 2016 · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro. 2016 · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis. 2016 · 2016
Cited alongside, same era.
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama. 2018 · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow. 2018 · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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