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Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
Object enhancement and extraction
Judith M S Prewitt · 1970
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Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid
Ron Kohavi · 1996
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Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger · 2001
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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A public domain dataset for human activity recognition using smartphones
D. Anguita, Alessandro Ghio, L. Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Machine Learning for First-Order Theorem Proving - Learning to Select a Good Heuristic
James P. Bridge, S. Holden, and Lawrence Charles Paulson · 2014
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The wonderful colors of the hematoxylin–eosin stain in diagnostic surgical pathology
John KC Chan · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Parameterized neural networks for high-energy physics
Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection
Konstantin Pogorelov, Kristin Ranheim Randel, Carsten Griwodz, Sigrun Losada Eskeland, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, Mathias Lux, Peter Thelin Schmidt, et al · 2017
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Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring Using Convolutional Neural Networks
Terry T. Um, Franz M. J. Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulić · 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
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Time series classification from scratch with deep neural networks: A strong baseline
Zhiguang Wang, Weizhong Yan, and Tim Oates · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A Efros · 2017
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Laming Chen, Guoxin Zhang, and Eric Zhou · 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
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou · 2019
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Conditional bert contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu · 2019
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Flows for simultaneous manifold learning and density estimation
Johann Brehmer and Kyle Cranmer · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, and Pranav Rajpurkar · 2021
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Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 2021
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SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Talip Ucar, Ehsan Hajiramezanali, and Lindsay Edwards · 2021
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Towards domain-agnostic contrastive learning
Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, and Quoc Le · 2021
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What Should Not Be Contrastive in Contrastive Learning
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Subject-aware contrastive learning for biosignals
Joseph Y Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, and Erdrin Azemi · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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A mutual information maximization perspective of language representation learning
Lingpeng Kong, Cyprien de Masson d’Autume, Lei Yu, Wang Ling, Zihang Dai, and Dani Yogatama · 2020
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Contrastive representation learning: A framework and review
Phuc H Le-Khac, Graham Healy, and Alan F Smeaton · 2020
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Contrastive representation learning for electroencephalogram classification
Mostafa Neo Mohsenvand, Mohammad Rasool Izadi, and Pattie Maes · 2020
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Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2021
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Neighborhood contrastive learning applied to online patient monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch · 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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Scarf: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 2022
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Why Do Self-Supervised Models Transfer? On the Impact of Invariance on Downstream Tasks
Linus Ericsson, Henry Gouk, and Timothy Hospedales · 2022
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STab: Self-supervised Learning for Tabular Data
Ehsan Hajiramezanali, Nathaniel Lee Diamant, Gabriele Scalia, and Max W Shen · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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MET: Masked Encoding for Tabular Data
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RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization
Yiqing Shen, Yulin Luo, Dinggang Shen, and Jing Ke · 2022
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SimMIM: A Simple Framework for Masked Image Modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views
Junbo Zhang and Kaisheng Ma · 2022
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Randall Balestriero · 2023
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A Cookbook of Self-Supervised Learning
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TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
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Benchmarking self-supervised learning on diverse pathology datasets
Mingu Kang, Heon Song, Seonwook Park, Donggeun Yoo, and Sérgio Pereira · 2023
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Time series contrastive learning with information-aware augmentations
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Decentralized federated learning through proxy model sharing
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