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Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning of images.
A comprehensive survey on graph neural networks
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Adversarial defense framework for graph neural network
Wang, S., Chen, Z., Ni, J., Yu, X., Li, Z., Chen, H., and Yu, P. S · 1905
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Multilevel graph partitioning schemes
Karypis, G. and Kumar, V · 1995
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Convolutional networks for images, speech, and time series
LeCun, Y., Bengio, Y., et al · 1995
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Probabilistic frame-based systems
Koller, D. and Pfeffer, A · 1998
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Learning probabilistic relational models
Friedman, N., Getoor, L., Koller, D., and Pfeffer, A · 1999
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Learning from labeled and unlabeled data using graph mincuts
Blum, A. and Chawla, S · 2001
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Partially labeled classification with markov random walks
Szummer, M. and Jaakkola, T · 2002
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Towards semi-supervised classification with Markov random fields
Zhu, X. and Ghahramani, Z · 2002
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Semi-supervised learning using randomized mincuts
Blum, A., Lafferty, J., Rwebangira, M. R., and Reddy, R · 2004
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Semi-supervised learning–a statistical physics approach
Getz, G., Shental, N., and Domany, E · 2006
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The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks
Azran, A · 2007
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Introduction to semi-supervised learning
Zhu, X. and Goldberg, A. B · 2009
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A tour of modern image filtering: New insights and methods, both practical and theoretical
Milanfar, P · 2012
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Seeing the bigger picture: How nodes can learn their place within a complex ad hoc network topology
Bertrand, A. and Moonen, M · 2013
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure
Sandryhaila, A. and Moura, J. M · 2014
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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Generative image inpainting with contextual attention
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T. S · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Adversarial attacks on neural networks for graph data
Zügner, D., Akbarnejad, A., and Günnemann, S · 2018
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Scaling and benchmarking self-supervised visual representation learning
Goyal, P., Mahajan, D., Gupta, A., and Misra, I · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
Cited alongside, same era.
Estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse
Wu, L., Laeuchli, J., Kalantzis, V., Stathopoulos, A., and Gallopoulos, E · 2016
Cited alongside, same era.
Bias-variance tradeoff of graph laplacian regularizer
Chen, P.-Y. and Liu, S · 2017
Cited alongside, same era.
Multi-task self-supervised visual learning
Doersch, C. and Zisserman, A · 2017
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Cited alongside, same era.
Adversarial attack on graph structured data
Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Karimi, M., Wu, D., Wang, Z., and Shen, Y · 2019
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Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X., and Beyer, L · 2019
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Gmnn: Graph markov neural networks
Qu, M., Bengio, Y., and Tang, J · 2019
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When does self-supervision improve few-shot learning?
Su, J.-C., Maji, S., and Hariharan, B · 2019
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Multi-stage self-supervised learning for graph convolutional networks
Sun, K., Zhu, Z., and Lin, Z · 2019
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Selfie: Self-supervised pretraining for image embedding
Trinh, T. H., Luong, M.-T., and Le, Q. V · 2019
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Graphmix: Regularized training of graph neural networks for semi-supervised learning
Verma, V., Qu, M., Lamb, A., Bengio, Y., Kannala, J., and Tang, J · 2019
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Adversarial robustness: From self-supervised pre-training to fine-tuning
Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., and Wang, Z · 2020
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Self-supervised learning for generalizable out-of-distribution detection
Mohseni, S., Pitale, M., Yadawa, J., and Wang, Z · 2020
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L 2 -gcn: Layer-wise and learned efficient training of graph convolutional networks
You, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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