Fetching the paper…
Reading the bibliography…
Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications.
Dabak, A.G.: A geometry for detection theory. In: PhD Thesis, Rice Unviersity (1992)
1992
Earlier work this paper cites.
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8
1992
Earlier work this paper cites.
Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine learning proceedings 1994, pp. 148–156. Elsevier (1994)
1994
Earlier work this paper cites.
Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: SIGIR’94. pp. 3–12. Springer (1994)
1994
Earlier work this paper cites.
Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: Proceedings of the twenty-first international conference on Machine learning. p. 79. ACM (2004)
2004
Earlier work this paper cites.
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the conference on empirical methods in natural language processing. pp. 1070–1079. Association for Computational Linguistics (2008)
2008
Earlier work this paper cites.
Bilgic, M., Getoor, L.: Link-based active learning. In: NIPS Workshop on Analyzing Networks and Learning with Graphs (2009)
2009
Earlier work this paper cites.
Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2372–2379. IEEE (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88
2010
Earlier work this paper cites.
Guo, Y.: Active instance sampling via matrix partition. In: Advances in Neural Information Processing Systems. pp. 802–810 (2010)
2010
Earlier work this paper cites.
Ebert, S., Fritz, M., Schiele, B.: Ralf: A reinforced active learning formulation for object class recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3626–3633 (2012)
2012
Earlier work this paper cites.
Settles, B.: Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 6
2012
Earlier work this paper cites.
Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: ICML Workshop on Challenges in Representation Learning (WREPL) (2013)
2013
Earlier work this paper cites.
Li, X., Guo, Y.: Adaptive active learning for image classification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 859–866 (2013)
2013
Earlier work this paper cites.
Luo, W., Schwing, A., Urtasun, R.: Latent structured active learning. In: Advances in Neural Information Processing Systems. pp. 728–736 (2013)
2013
Cited alongside, same era.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2015)
2015
Cited alongside, same era.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
2015
Cited alongside, same era.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3213–3223 (2016)
2016
Cited alongside, same era.
Konyushkova, K., Uijlings, J., Lampert, C.H., Ferrari, V.: Learning intelligent dialogs for bounding box annotation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Later among the works it cites.
Kuo, W., Häne, C., Yuh, E.L., Mukherjee, P., Malik, J.: Cost-sensitive active learning for intracranial hemorrhage detection. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III. pp. 715–723 (2018)
2018
Later among the works it cites.
Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., Rother, C.: CEREALS - cost-effective region-based active learning for semantic segmentation. In: British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018 (2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fang, M., Li, Y., Cohn, T.: Learning how to active learn: A deep reinforcement learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 595–605 (2017)
2017
Cited alongside, same era.
Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1183–1192. JMLR. org (2017)
2017
Cited alongside, same era.
Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1183–1192. JMLR. org (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Cir. and Sys. for Video Technol. 27
2017
Cited alongside, same era.
Woodward, M., Finn, C.: Active one-shot learning. In: NIPS Deep RL Workshop (2017)
2017
Cited alongside, same era.
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 399–407. Springer (2017)
2017
Cited alongside, same era.
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: A deep active learning framework for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. pp. 399–407. Springer (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
Rosenfeld, A., Zemel, R.S., Tsotsos, J.K.: The elephant in the room. CoRR abs/1808.03305
2018
Later among the works it cites.
Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=H1aIuk-RW
2018
Later among the works it cites.
2018
Later among the works it cites.
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 405–420 (2018)
2018
Later among the works it cites.
Zhou, K., Qiao, Y., Xiang, T.: Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward (2018)
2018
Later among the works it cites.
Arazo, E., Ortego, D., Albert, P., O’Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning (2019)
2019
Later among the works it cites.
Kasarla, T., Nagendar, G., Hegde, G., Balasubramanian, V., Jawahar, C.: Region-based active learning for efficient labeling in semantic segmentation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1109–1118 (Jan 2019). https://doi.org/10.1109/WACV.2019.00124
2019
Later among the works it cites.
Liu, Z., Wang, J., Gong, S., Lu, H., Tao, D.: Deep reinforcement active learning for human-in-the-loop person re-identification. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Sinha, S., Ebrahimi, S., Darrell, T.: Variational adversarial active learning. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Yoo, D., Kweon, I.S.: Learning loss for active learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.