Fetching the paper…
Reading the bibliography…
Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input.
Discrete cosine transform
Nasir Ahmed, T_ Natarajan, and Kamisetty R Rao · 1974
Earlier work this paper cites.
Self-organizing neural network that discovers surfaces in random-dot stereograms
Suzanna Becker and Geoffrey E Hinton · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Vicinal risk minimization
Olivier Chapelle, Jason Weston, Léon Bottou, and Vladimir Vapnik · 2001
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
Earlier work this paper cites.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
Earlier work this paper cites.
A survey on human activity recognition using wearable sensors
Oscar D Lara and Miguel A Labrador · 2012
Earlier work this paper cites.
Introducing a new benchmarked dataset for activity monitoring
Attila Reiss and Didier Stricker · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Librispeech: An asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
Earlier work this paper cites.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
Earlier work this paper cites.
Data augmentation generative adversarial networks, 2017
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
Earlier work this paper cites.
Dataset augmentation in feature space, 2017
Terrance DeVries and Graham W. Taylor · 2017
Earlier work this paper cites.
Increasing the robustness of cnn acoustic models using autoregressive moving average spectrogram features and channel dropout
György Kovács, László Tóth, Dirk Van Compernolle, and Sriram Ganapathy · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Voxceleb: a large-scale speaker identification dataset, 2017
Arsha Nagrani, Joon Son Chung, and Andrew Zisserman · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
Cited alongside, same era.
Learning to compose domain-specific transformations for data augmentation
Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, and Christopher Ré · 2017
Cited alongside, same era.
A bayesian data augmentation approach for learning deep models, 2017
Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, and Ian Reid · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
Later among the works it cites.
Improving robustness without sacrificing accuracy with patch gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk · 2019
Later among the works it cites.
Speech model pre-training for end-to-end spoken language understanding, 2019
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio · 2019
Later among the works it cites.
Specaugment: A simple data augmentation method for automatic speech recognition
Daniel S Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D Cubuk, and Quoc V Le · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Interpreting and explaining deep neural networks for classification of audio signals, 2018
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
Cited alongside, same era.
Adversarial contrastive estimation
Avishek Joey Bose, Huan Ling, and Yanshuai Cao · 2018
Cited alongside, same era.
Gan augmentation: Augmenting training data using generative adversarial networks, 2018
Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, and Daniel Rueckert · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Convolutional neural networks for human activity recognition using body-worn sensors
Fernando Moya Rueda, René Grzeszick, Gernot A Fink, Sascha Feldhorst, and Michael Ten Hompel · 2018
Cited alongside, same era.
Later among the works it cites.
Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang · 2019
Later among the works it cites.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Later among the works it cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples, 2019
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Later among the works it cites.
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2019
Later among the works it cites.
Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
Closest in time.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Closest in time.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Closest in time.
Affinity and diversity: Quantifying mechanisms of data augmentation
Raphael Gontijo-Lopes, Sylvia J Smullin, Ekin D Cubuk, and Ethan Dyer · 2020
Closest in time.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
Closest in time.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Closest in time.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2020
Closest in time.
Data augmenting contrastive learning of speech representations in the time domain
Eugene Kharitonov, Morgane Rivière, Gabriel Synnaeve, Lior Wolf, Pierre-Emmanuel Mazaré, Matthijs Douze, and Emmanuel Dupoux · 2020
Closest in time.
Adversarial self-supervised contrastive learning, 2020
Minseon Kim, Jihoon Tack, and Sung Ju Hwang · 2020
Closest in time.
Deep representation learning in speech processing: Challenges, recent advances, and future trends
Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak, Junaid Qadir, and Björn W Schuller · 2020
Closest in time.
Automatic shortcut removal for self-supervised representation learning
Matthias Minderer, Olivier Bachem, Neil Houlsby, and Michael Tschannen · 2020
Closest in time.
Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
Closest in time.
Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
Senthil Purushwalkam and Abhinav Gupta · 2020
Closest in time.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Closest in time.
Learning perturbation sets for robust machine learning, 2020
Eric Wong and J. Zico Kolter · 2020
Closest in time.
On mutual information in contrastive learning for visual representations
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, and Noah Goodman · 2020
Closest in time.