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This paper presents Contrastive Reconstruction, ConRec - a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification
RA Welikala, Jamshid Dehmeshki, Andreas Hoppe, V Tah, S Mann, Thomas H Williamson, and SA Barman. 2014 · 2014
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Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision . 1422–1430
Carl Doersch, Abhinav Gupta, and Alexei A Efros. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
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Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy
Roshan A Welikala, Muhammad Moazam Fraz, Jamshid Dehmeshki, Andreas Hoppe, V Tah, S Mann, Thomas H Williamson, and Sarah A Barman. 2015 · 2015
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Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2536–2544
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
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Colorful image colorization. In European conference on computer vision . Springer, 649–666
Richard Zhang, Phillip Isola, and Alexei A Efros. 2016 · 2016
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Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 4438–4446
Jianlong Fu, Heliang Zheng, and Tao Mei. 2017 · 2017
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Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1125–1134
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017 · 2017
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018 · 2018
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Improving landmark localization with semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1546–1555
Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, and Jan Kautz. 2018 · 2018
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Audio-visual scene analysis with self-supervised multisensory features. In Proceedings of the European Conference on Computer Vision (ECCV) . 631–648
Andrew Owens and Alexei A Efros. 2018 · 2018
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Cross-domain adaptation for animal pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 9498–9507
Jinkun Cao, Hongyang Tang, Hao-Shu Fang, Xiaoyong Shen, Cewu Lu, and Yu-Wing Tai. 2019 · 2019
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Destruction and construction learning for fine-grained image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5157–5166
Yue Chen, Yalong Bai, Wei Zhang, and Tao Mei. 2019 · 2019
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Rethinking imagenet pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 4918–4927
Kaiming He, Ross Girshick, and Piotr Dollár. 2019 · 2019
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Self-supervised audio representation learning for mobile devices
Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry, and Dominik Roblek. 2019 · 2019
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Multimodal self-supervised learning for medical image analysis
Aiham Taleb, Christoph Lippert, Tassilo Klein, and Moin Nabi. 2019 · 2019
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Deep learning for fine-grained image analysis: A survey
Xiu-Shen Wei, Jianxin Wu, and Quan Cui. 2019 · 2019
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Parts2Whole: Self-supervised Contrastive Learning via Reconstruction
Ruibin Feng, Zongwei Zhou, Michael B Gotway, and Jianming Liang. 2020 · 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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Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
Bijan Haney and Alexander Lavin. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 6707–6717
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Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B Gotway, and Jianming Liang. 2019 · 2019
Cited alongside, same era.
https://www.kaggle.com/c/aptos2019-blindness-detection/
Aptos 2019 Kaggle Challenge 2019 · 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 · 2020
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The devil is in the channels: Mutual-channel loss for fine-grained image classification
Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, and Yi-Zhe Song. 2020 · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020b · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020c · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020a · 2020
Cited alongside, same era.
Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology
Olivier Dehaene, Axel Camara, Olivier Moindrot, Axel de Lavergne, and Pierre Courtiol. 2020 · 2020
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Ishan Misra and Laurens van der Maaten. 2020 · 2020
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Learning and Evaluating Representations for Deep One-class Classification
Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister. 2020 · 2020
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3D Self-Supervised Methods for Medical Imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, and Christoph Lippert. 2020 · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. 2020 · 2020
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Inpainting Networks Learn to Separate Cells in Microscopy Images. In The British Machine Vision Conference (BMVC)
Steffen Wolf, Fred A Hamprecht, Jan Funke, HHMI Janelia, and VA Ashburn. 2020 · 2020
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CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y Ng, and Pranav Rajpurkar. 2021 · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Attribute-aware attention model for fine-grained representation learning. In Proceedings of the 26th ACM international conference on Multimedia . 2040–2048
Kai Han, Jianyuan Guo, Chao Zhang, and Mingjian Zhu. 2018 · 2048
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