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Self- and semi-supervised machine learning techniques leverage unlabeled data for improving downstream task performance.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Mutual exclusivity loss for semi-supervised deep learning,” in 2016 IEEE International Conference on Image Processing (ICIP) . IEEE, 2016, pp. 1908–1912
1912
Earlier work this paper cites.
Y. Grandvalet and Y. Bengio, “Semi-supervised learning by entropy minimization,” in Advances in Neural Information Processing Systems , L. Saul, Y. Weiss, and L. Bottou, Eds., vol. 17. MIT Press, 2005
2005
Earlier work this paper cites.
L. Bruzzone, M. Chi, and M. Marconcini, “A novel transductive svm for semisupervised classification of remote-sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 44, no. 11, pp. 3363–3373, 2006
2006
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 2. IEEE, 2006, pp. 1735–1742
2006
Earlier work this paper cites.
G. Camps-Valls, T. V. B. Marsheva, and D. Zhou, “Semi-supervised graph-based hyperspectral image classification,” IEEE transactions on Geoscience and Remote Sensing , vol. 45, no. 10, pp. 3044–3054, 2007
2007
Earlier work this paper cites.
D. Tuia and G. Camps-Valls, “Semisupervised remote sensing image classification with cluster kernels,” IEEE Geoscience and Remote Sensing Letters , vol. 6, no. 2, pp. 224–228, 2009
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Toronto, Ontario, Tech. Rep. 0, 2009
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision , vol. 88, no. 2, pp. 303–338, Jun. 2010
2010
Earlier work this paper cites.
U. Maulik and D. Chakraborty, “A self-trained ensemble with semisupervised svm: An application to pixel classification of remote sensing imagery,” Pattern Recognition , vol. 44, no. 3, pp. 615–623, 2011
2011
Earlier work this paper cites.
R. Torres, P. Snoeij, D. Geudtner, D. Bibby, M. Davidson, E. Attema, P. Potin, B. Rommen, N. Floury, M. Brown et al. , “Gmes sentinel-1 mission,” Remote sensing of environment , vol. 120, pp. 9–24, 2012
2012
Earlier work this paper cites.
——, “Learning with transductive svm for semisupervised pixel classification of remote sensing imagery,” ISPRS journal of photogrammetry and remote sensing , vol. 77, pp. 66–78, 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2014
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015, pp. 448–456
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2015
2015
Earlier work this paper cites.
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros, “Context encoders: Feature learning by inpainting,” in Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in ECCV , 2016
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in European conference on computer vision . Springer, 2016, pp. 69–84
2016
Earlier work this paper cites.
M. Huh, P. Agrawal, and A. A. Efros, “What makes imagenet good for transfer learning?” CoRR , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” in Advances in Neural Information Processing Systems , vol. 29, 2016
2016
Earlier work this paper cites.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google earth engine: Planetary-scale geospatial analysis for everyone,” Remote sensing of Environment , vol. 202, pp. 18–27, 2017
2017
Earlier work this paper cites.
D. Lin, K. Fu, Y. Wang, G. Xu, and X. Sun, “Marta gans: Unsupervised representation learning for remote sensing image classification,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 11, pp. 2092–2096, 2017
2017
Earlier work this paper cites.
X. Lu, X. Zheng, and Y. Yuan, “Remote sensing scene classification by unsupervised representation learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 9, pp. 5148–5157, 2017
2017
Earlier work this paper cites.
B. Chaudhuri, B. Demir, S. Chaudhuri, and L. Bruzzone, “Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 2, pp. 1144–1158, 2017
2017
Earlier work this paper cites.
A. E. Maxwell, T. A. Warner, B. C. Vanderbilt, and C. A. Ramezan, “Land cover classification and feature extraction from national agriculture imagery program (naip) orthoimagery: A review,” Photogrammetric Engineering & Remote Sensing , vol. 83, no. 11, pp. 737–747, 2017
2017
Cited alongside, same era.
X. X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, and F. Fraundorfer, “Deep learning in remote sensing: A comprehensive review and list of resources,” IEEE Geoscience and Remote Sensing Magazine , vol. 5, no. 4, pp. 8–36, 2017
2017
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Cited alongside, same era.
J. Wang and L. Perez, “The effectiveness of data augmentation in image classification using deep learning,” Convolutional Neural Networks Vis. Recognit , vol. 11, pp. 1–8, 2017
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 113–123
2019
Later among the works it cites.
C. Robinson, L. Hou, K. Malkin, R. Soobitsky, J. Czawlytko, B. Dilkina, and N. Jojic, “Large scale high-resolution land cover mapping with multi-resolution data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 726–12 735
2019
Later among the works it cites.
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 596–608
2020
Later among the works it cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
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2017
Cited alongside, same era.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017
2017
Cited alongside, same era.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018. [Online]. Available: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
2018
Cited alongside, same era.
N. Komodakis and S. Gidaris, “Unsupervised representation learning by predicting image rotations,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
W. Han, R. Feng, L. Wang, and Y. Cheng, “A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 145, pp. 23–43, 2018
2018
Cited alongside, same era.
H. Wu and S. Prasad, “Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels,” Pattern Recognition , vol. 74, pp. 212–224, 2018
2018
Cited alongside, same era.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , September 2018
2018
Cited alongside, same era.
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, and I. Goodfellow, “Realistic evaluation of deep semi-supervised learning algorithms,” in Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., vol. 31. Curran Associates, Inc., 2018
2018
Cited alongside, same era.
H. Caesar, J. Uijlings, and V. Ferrari, “Coco-stuff: Thing and stuff classes in context,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1209–1218
2018
Cited alongside, same era.
2020
Later among the works it cites.
Z. Zhao, Z. Luo, J. Li, C. Chen, and Y. Piao, “When self-supervised learning meets scene classification: Remote sensing scene classification based on a multitask learning framework,” Remote Sensing , vol. 12, no. 20, p. 3276, 2020
2020
Later among the works it cites.
S. Vincenzi, A. Porrello, P. Buzzega, M. Cipriano, F. Pietro, C. Roberto, I. Carla, C. Annamaria, and S. Calderara, “The color out of space: learning self-supervised representations for earth observation imagery,” in 25th International Conference on Pattern Recognition , 2020
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Later among the works it cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko, “Bootstrap your own latent - a new approach to self-supervised learning,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33, 2020, pp. 21 271–21 284
2020
Later among the works it cites.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” Advances in Neural Information Processing Systems , vol. 33, pp. 9912–9924, 2020
2020
Later among the works it cites.
J. Kang, R. Fernandez-Beltran, P. Duan, S. Liu, and A. J. Plaza, “Deep unsupervised embedding for remotely sensed images based on spatially augmented momentum contrast,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 3, pp. 2598–2610, 2020
2020
Later among the works it cites.
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel, “Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring,” in 8th International Conference on Learning Representations, ICLR , 2020
2020
Later among the works it cites.
J. Li, R. Socher, and S. C. H. Hoi, “Dividemix: Learning with noisy labels as semi-supervised learning,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 , 2020
2020
Later among the works it cites.
X. Sun, A. Shi, H. Huang, and H. Mayer, “Bas4net: Boundary-aware semi-supervised semantic segmentation network for very high resolution remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 5398–5413, 2020
2020
Later among the works it cites.
D. Hong, N. Yokoya, G.-S. Xia, J. Chanussot, and X. X. Zhu, “X-modalnet: A semi-supervised deep cross-modal network for classification of remote sensing data,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 167, pp. 12–23, 2020
2020
Later among the works it cites.
R. Pires de Lima and K. Marfurt, “Convolutional neural network for remote-sensing scene classification: Transfer learning analysis,” Remote Sensing , vol. 12, no. 1, p. 86, 2020
2020
Later among the works it cites.
D. Bonafilia, B. Tellman, T. Anderson, and E. Issenberg, “Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2020
2020
Later among the works it cites.
“Naip imagery,” https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/ , accessed: 2021-08-13
2021
Closest in time.
H. Jung, Y. Oh, S. Jeong, C. Lee, and T. Jeon, “Contrastive self-supervised learning with smoothed representation for remote sensing,” IEEE Geoscience and Remote Sensing Letters , 2021
2021
Closest in time.
V. Stojnic and V. Risojevic, “Self-supervised learning of remote sensing scene representations using contrastive multiview coding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1182–1191
2021
Closest in time.
K. Ayush, B. Uzkent, C. Meng, K. Tanmay, M. Burke, D. Lobell, and S. Ermon, “Geography-aware self-supervised learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 181–10 190
2021
Closest in time.
M. Leenstra, D. Marcos, F. Bovolo, and D. Tuia, “Self-supervised pre-training enhances change detection in sentinel-2 imagery,” in Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings , ser. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12667, 2021, pp. 578–590
2021
Closest in time.
W. Li, H. Chen, and Z. Shi, “Semantic segmentation of remote sensing images with self-supervised multitask representation learning,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 6438–6450, 2021
2021
Closest in time.
“Land cover data project 2013/2014,” Aug 2020, accessed: 2021-02-11. [Online]. Available: https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/
2021
Closest in time.
“Earth engine naip imagery,” https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ , accessed: 2021-08-13
2021
Closest in time.