survey

A Survey on Active Deep Learning: From Model Driven to Data Driven

Authors:

Lei ZhaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 10s

Article No.: 221, Pages 1 - 34

https://doi.org/10.1145/3510414

Published: 13 September 2022 Publication History

Abstract

Which samples should be labelled in a large dataset is one of the most important problems for the training of deep learning. So far, a variety of active sample selection strategies related to deep learning have been proposed in the literature. We defined them as Active Deep Learning (ADL) only if their predictor or selector is a deep model, where the basic learner is called the predictor and the labeling schemes are called the selector. In this survey, we categorize ADL into model-driven ADL and data-driven ADL by whether its selector is model driven or data driven. We also introduce the different characteristics of the two major types of ADL, respectively. We summarized three fundamental factors in the designation of a selector. We pointed out that, with the development of deep learning, the selector in ADL also is experiencing the stage from model driven to data driven. The advantages and disadvantages between data-driven ADL and model-driven ADL are thoroughly analyzed. Furthermore, different sub-classes of data-drive or model-driven ADL are also summarized and discussed emphatically. Finally, we survey the trend of ADL from model driven to data driven.

References

[1]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE Computer Society, 770–778.

[2]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE Computer Society, 248–255.

[3]

Peng Liu, Kim-Kwang Raymond Choo, Lizhe Wang, and Fang Huang. 2017. SVM or deep learning? A comparative study on remote sensing image classification. Soft Comput. 21, 23 (2017), 7053–7065.

Digital Library

[4]

Peng Liu, Liping Di, Qian Du, and Lizhe Wang. 2018. Remote sensing big data: Theory, methods and applications. Remote Sensing 10, 5 (2018), 711.

[5]

David Cohn, Les Atlas, and Richard Ladner. 1994. Improving generalization with active learning. Mach. Learn. 15, 2 (1994), 201–221.

[6]

Burr Settles. 2009. Active Learning Literature Survey. Technical Report. University of Wisconsin—Madison Department of Computer Sciences.

[7]

Valerii Fedorov. 2010. Optimal experimental design. Wiley Interdisc. Rev.: Comput. Stat. 2, 5 (2010), 581–589.

Digital Library

[8]

Fredrik Olsson. 2009. A literature survey of active machine learning in the context of natural language processing. https://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/materials/SICS-T--2009-06--SE.pdf.

[9]

Mehdi Elahi, Francesco Ricci, and Neil Rubens. 2016. A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20 (2016), 29–50.

Digital Library

[10]

D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari. 2011. A survey of active learning algorithms for supervised remote sensing image classification. IEEE J. Select. Top. Sign. Process. 5, 3 (2011), 606–617.

[11]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2020. A survey of deep active learning. arXiv:2009.00236. Retrieved from https://arxiv.org/abs/2009.00236.

[12]

Christopher Schröder and Andreas Niekler. 2020. A survey of active learning for text classification using deep neural networks.arXiv:2008.07267. Retrieved from https://arxiv.org/abs/2008.07267.

[13]

Samuel Budd, Emma C. Robinson, and Bernhard Kainz. 2021. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis 71 (2021), 102062.

[14]

Yazhou Yang and Marco Loog. 2018. A benchmark and comparison of active learning for logistic regression. Pattern Recogn. 83 (2018), 401–415. https://www.sciencedirect.com/science/article/pii/S0031320318302140.

Digital Library

[15]

Jamshid Sourati, Ali Gholipour, Jennifer G. Dy, Xavier Tomas-Fernandez, Sila Kurugol, and Simon K. Warfield. 2019. Intelligent labeling based on fisher information for medical image segmentation using deep learning. IEEE Trans. Med. Imag. 38, 11 (2019), 2642–2653.

[16]

Jordan T. Ash and Ryan P. Adams. 2019. On the difficulty of warm-starting neural network training. arxiv:1910.08475. Retrieved from http://arxiv.org/abs/1910.08475.

[17]

Jin Yuan, Xingxing Hou, Yaoqiang Xiao, Da Cao, Weili Guan, and Liqiang Nie. 2019. Multi-criteria active deep learning for image classification. Knowl. Bas. Syst. 172 (2019), 86–94.

Digital Library

[18]

P. Liu, H. Zhang, and K. B. Eom. 2017. Active deep learning for classification of hyperspectral images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 10, 2 (February 2017), 712–724.

[19]

Xiaoming Lv, Fajie Duan, Jia-Jia Jiang, Xiao Fu, and Lin Gan. 2020. Deep active learning for surface defect detection. Sensors 20, 6 (2020).

[20]

Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017. Deep bayesian active learning with image data. arXiv:1703.02910. Retrieved from https://arxiv.org/abs/1703.02910.

[21]

Donggeun Yoo and In So Kweon. 2019. Learning loss for active learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). 93–102.

[22]

Mark Woodward and Chelsea Finn. 2017. Active one-shot learning. arXiv:1702.06559. Retrieved from http://arxiv.org/abs/1702.06559.

[23]

Kunkun Pang, Mingzhi Dong, Yang Wu, and Timothy M. Hospedales. 2018. Meta-learning transferable active learning policies by deep reinforcement learning. arxXiv:1806.04798. Retrieved from http://arxiv.org/abs/1806.04798.

[24]

Sachin Ravi and Hugo Larochelle. 2018. Meta-learning for batch mode active learning. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18). OpenReview.net.

[25]

Gabriella Contardo, Ludovic Denoyer, and Thierry Artières. 2017. A meta-learning approach to one-step active learning. arXiv:1706.08334. Retrieved from http://arxiv.org/abs/1706.08334.

[26]

Jia-Jie Zhu and José Bento. 2017. Generative adversarial active learning. arXiv:1702.07956. Retrieved from http://arxiv.org/abs/1702.07956.

[27]

Christoph Mayer and Radu Timofte. 2020. Adversarial sampling for active learning. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. 3071–3079.

[28]

Melanie Ducoffe and Frederic Precioso. 2018. Adversarial active learning for deep networks: A margin based approach. arXiv:1802.09841. Retrieved from https://arxiv.org/abs/1802.09841.

[29]

Ajay J. Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. 2009. Multi-class active learning for image classification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE Computer Society, 2372–2379.

[30]

Vít Ruzicka, Stefano D’Aronco, Jan Dirk Wegner, and Konrad Schindler. 2020. Deep active learning in remote sensing for data efficient change detection. CoRR abs/2008.11201.

[31]

Buyu Liu and Vittorio Ferrari. 2017. Active learning for human pose estimation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). IEEE Computer Society, 4373–4382.

[32]

Seho Kee, Enrique del Castillo, and George Runger. 2018. Query-by-committee improvement with diversity and density in batch active learning. Inf. Sci. 454-455 (2018), 401–418.

[33]

Xiangyong Cao, Jing Yao, Zongben Xu, and Deyu Meng. 2020. Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans. Geosci. Remote Sens. (2020), 1–13.

[34]

G. Joo and C. Kim. 2018. MIDAS: Model-independent training data selection under cost constraints. IEEE Access 6 (2018), 74462–74474.

[35]

Yuhao Wu, Yuzhou Fang, Shuaikang Shang, Jing Jin, Lai Wei, and Haizhou Wang. 2021. A novel framework for detecting social bots with deep neural networks and active learning. Knowl.-Bas. Syst. 211 (2021), 106525.

[36]

Peng Peng, Wenjia Zhang, Yi Zhang, Yanyan Xu, Hongwei Wang, and Heming Zhang. 2020. Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis. Neurocomputing 407 (2020), 232–245.

[37]

Cheng Deng, Yumeng Xue, Xianglong Liu, Chao Li, and Dacheng Tao. 2019. Active transfer learning network: A unified deep joint spectral-spatial feature learning model for hyperspectral image classification. IEEE Trans. Geosci. Remote. Sens. 57, 3 (2019), 1741–1754.

[38]

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi. 2016. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imag. 35, 1 (2016), 119–130.

[39]

Yan Tian and Guohua Cheng et al. 2020. Joint temporal context exploitation and active learning for video segmentation. Pattern Recogn. 100 (2020), 107158.

Digital Library

[40]

Pawe Ksieniewicz, Micha Woniak, Bogusaw Cyganek, Andrzej Kasprzak, and Krzysztof Walkowiak. 2019. Data stream classification using active learned neural networks. Neurocomputing 353 (2019), 74–82. Recent Advancements in Hybrid Artificial Intelligence Systems.

Digital Library

[41]

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016. DeepFool: A simple and accurate method to fool deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE Computer Society, 2574–2582.

[42]

Ozan Sener and Silvio Savarese. 2017. A geometric approach to active learning for convolutional neural networks. arXiv: abs/1708.00489. Retrieved from https://arxiv.org/abs/1708.00489.

[43]

Ozan Sener and Silvio Savarese. 2017. Active learning for convolutional neural networks: A core-set approach. arXiv:1708.00489. Retrieved from https://arxiv.org/abs/1708.00489.

[44]

Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, and Shadab Khan. 2020. Towards robust and reproducible active learning using neural networks (unpublished).

[45]

R. A. Fisher. 1992. On the Mathematical Foundations of Theoretical Statistics. Springer, New York, NY, 11–44.

[46]

Kenji Fukumizu. 2000. Statistical active learning in multilayer perceptrons. IEEE Trans. Neural Netw. Learn. Syst. 11, 1 (2000), 17–26.

Digital Library

[47]

Tong Zhang. 2000. The value of unlabeled data for classification problems. In Proceedings of the 17th International Conference on Machine Learning. Morgan Kaufmann, 1191–1198.

[48]

Burr Settles, Mark Craven, and Soumya Ray. 2008. Multiple-instance active learning. In Advances in Neural Information Processing Systems. MIT Press, 1289–1296.

Digital Library

[49]

Steven C. H. Hoi, Rong Jin, and Michael R. Lyu. 2009. Batch mode active learning with applications to text categorization and image retrieval. IEEE Trans. Knowl. Data Eng. 21, 9 (2009), 1233–1248.

Digital Library

[50]

Kamalika Chaudhuri, Sham M. Kakade, and Praneeth Netrapalli, et al. 2015. Convergence rates of active learning for maximum likelihood estimation. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, Corinna Cortes, Neil D. Lawrence, and Daniel D. Lee, et al. (Eds.). 1090–1098.

[51]

Jamshid Sourati, Murat Akçakaya, and Todd K. Leen, et al. 2017. Asymptotic analysis of objectives based on fisher information in active learning. J. Mach. Learn. Res. 18 (2017), 34:1–34:41.

[52]

Ye Zhang and Matthew Lease, et al. 2017. Active discriminative text representation learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, Satinder P. Singh and Shaul Markovitch (Eds.). AAAI Press, 3386–3392.

[53]

Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2019. Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv:1906.03671. Retrieved from http://arxiv.org/abs/1906.03671.

[54]

Chin-Chun Chang and Po-Yi Lin. 2015. Active learning for semi-supervised clustering based on locally linear propagation reconstruction. Neural Netw. 63 (2015), 170–184.

Digital Library

[55]

Lijun Zhang, Chun Chen, and Jiajun Bu, et al. 2011. Active learning based on locally linear reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 33, 10 (2011), 2026–2038.

Digital Library

[56]

Oriane Simoni, Mateusz Budnik, Yannis Avrithis, and Guillaume Gravier. 2019. Rethinking deep active learning: Using unlabeled data at model training. arXiv:cs.CV/1911.08177. Retrieved from https://arxiv.org/abs/1911.08177.

[57]

Andrew McCallum and Kamal Nigam. 1998. Employing EM and pool-based active learning for text classification. In Proceedings of the 15th International Conference on Machine Learning (ICML’98). Morgan Kaufmann, San Francisco, CA, 350–358.

[58]

Yazhou Yang and Marco Loog. 2019. Single shot active learning using pseudo annotators. Pattern Recogn. 89 (2019), 22–31.

[59]

Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, and Danny Z. Chen. 2017. Suggestive annotation: A deep active learning framework for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, 399–407.

Digital Library

[60]

Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). IEEE Computer Society, 3431–3440.

[61]

Ping Zhong, Zhiqiang Gong, Shutao Li, and Carola-Bibiane Schönlieb. 2017. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans. Geosci. Remote. Sens. 55, 6 (2017), 3516–3530.

[62]

Xiaofeng Cao. 2020. A divide-and-conquer approach to geometric sampling for active learning. Expert Syst. Appl. 140 (2020).

Digital Library

[63]

P. Ruiz and J. Mateos, et al. 2014. Bayesian active remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 52, 4 (2014), 2186–2196.

[64]

S. Sun, P. Zhong, H. Xiao, and R. Wang. 2015. An MRF model-based active learning framework for the spectral-spatial classification of hyperspectral imagery. IEEE J. Select. Top. Sign. Process. 9, 6 (2015), 1074–1088.

[65]

J. M. Haut, M. E. Paoletti, J. Plaza, J. Li, and A. Plaza. 2018. Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach. IEEE Trans. Geosci. Remote Sens. 56, 11 (Nov 2018), 6440–6461.

[66]

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural networks. arXiv:1505.05424. Retrieved from https://arxiv.org/abs/1505.05424.

[67]

Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, and José Miguel Hernández-Lobato. 2019. Bayesian batch active learning as sparse subset approximation. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems (NeurIPS’19), Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 6356–6367.

[68]

Jonathan Gordon and Jos Miguel Hernndez-Lobato. 2020. Combining deep generative and discriminative models for bayesian semi-supervised learning. Pattern Recogn. 100 (2020), 107156.

Digital Library

[69]

Firat Ozdemir and Zixuan Peng, et al. 2020. Active learning for segmentation based on bayesian sample queries. Knowl.-Bas. Syst. (2020), 106531.

[70]

Wen Shu, Peng Liu, Guojin He, and Guizhou Wang. 2019. Hyperspectral image classification using spectral-spatial features with informative samples. IEEE Access 7 (2019), 20869–20878.

[71]

Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, and Tomas Pfister. 2020. Consistency-based semi-supervised active learning: Towards minimizing labeling cost. arXiv:cs.LG/1910.07153. Retrieved from https://arxiv.org/abs/1910.07153.

[72]

David Muñoz and Camilo Narváez, et al. 2020. Incremental learning model inspired in rehearsal for deep convolutional networks. Knowl.-Bas. Syst. 208 (2020), 106460.

[73]

Luiz F. S. Coletta, Moacir Ponti, Eduardo R. Hruschka, Ayan Acharya, and Joydeep Ghosh. 2019. Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing 358 (2019), 150–165.

Digital Library

[74]

Soumi Das, Sayan Mandal, Ashwin Bhoyar, Madhumita Bharde, Niloy Ganguly, Suparna Bhattacharya, and Sourangshu Bhattacharya. 2020. Multi-criteria online frame-subset selection for autonomous vehicle videos. Pattern Recogn. Lett. 133 (2020), 349–355.

[75]

Tianxiang Yin, Ningzhong Liu, and Han Sun. 2020. Self-paced active learning for deep CNNs via effective loss function. Neurocomputing (2020).

[76]

Sergio Matiz and Kenneth E. Barner. 2019. Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification. Pattern Recogn. 90 (2019), 172–182.

Digital Library

[77]

William H. Beluch, Tim Genewein, Andreas Nürnberger, and Jan M. Köhler. 2018. The power of ensembles for active learning in image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9368–9377.

[78]

Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. 2017. The Mnist Database of Handwritten Digits. Technical Report. Retrieved from http://yann.lecun.com/exdb/mnist.

[79]

Alex Krizhevsky. 2009. Learning Multiple Layers of Features from Tiny Images. Technical Report.

[80]

Changjian Shui, Fan Zhou, et al. 2020. Deep active learning: Unified and principled method for query and training. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS’20), Silvia Chiappa and Roberto Calandra (Eds.), Vol. 108. PMLR, 1308–1318.

[81]

Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J. Storkey. 2020. Meta-learning in neural networks: A survey. CoRR abs/2004.05439.

[82]

Li Chen, Honglan Huang, Yanghe Feng, Guangquan Cheng, Jincai Huang, and Zhong Liu. 2020. Active one-shot learning by a deep q-network strategy. Neurocomputing 383 (2020), 324–335.

Digital Library

[83]

Philip Bachman, Alessandro Sordoni, and Adam Trischler. 2017. Learning algorithms for active learning. In Proceedings of the 34th International Conference on Machine Learning (ICML’17), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 301–310.

[84]

Oriol Vinyals, Charles Blundell, et al. 2016. Matching networks for one shot learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 3630–3638.

[85]

S. Zagoruyko and N. Komodakis. 2015. Learning to compare image patches via convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 4353–4361.

[86]

Gregory R. Koch. 2015. Siamese neural networks for one-shot image recognition.

[87]

F. Sung, Y. Yang, et al. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1199–1208.

[88]

Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17). OpenReview.net.

[89]

Luo Zhang, Peng Liu, Lei Zhao, Guizhou Wang, Wangfeng Zhang, and Jianbo Liu. 2021. Air quality predictions with a semi-supervised bidirectional LSTM neural network. Atmos. Poll. Res. 12, 1 (2021), 328–339.

[90]

Ksenia Konyushkova, Raphael Sznitman, et al. 2017. Learning active learning from data. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, Isabelle Guyon and Ulrike von Luxburg, et al (Eds.). 4225–4235.

[91]

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016. Meta-learning with memory-augmented neural networks. In Proceedings of the 33nd International Conference on Machine Learning (ICML’16), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.), Vol. 48. 1842–1850.

[92]

Andreas Kvistad, Massimiliano Ruocco, Eliezer de Souza da Silva, and Erlend Aune. 2019. Augmented memory networks for streaming-based active one-shot learning. arXiv:1909.01757. Retrieved from http://arxiv.org/abs/1909.01757.

[93]

Richard Stuart Sutton. 1984. Temporal credit assignment in reinforcement learning.

[94]

Meng Fang, Yuan Li, and Trevor Cohn. 2017. Learning how to active learn: A deep reinforcement learning approach. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17), Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 595–605.

[95]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529–533.

[96]

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning. PMLR, 1928–1937.

Digital Library

[97]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv:1312.5602. Retrieved from https://arxiv.org/abs/1312.5602.

[98]

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. 2016. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning. PMLR, 1995–2003.

Digital Library

[99]

Honglan Huang, Yanghe Feng, Jincai Huang, Jiarui Zhang, and Li Chen. 2019. A reinforcement one-shot active learning approach for aircraft type recognition. IEEE Access 7 (2019), 147204–147214.

[100]

Adriana Romero, Pierre Luc Carrier, et al. 2017. Diet networks: Thin parameters for fat genomics. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17). OpenReview.net.

[101]

Paul Budnarain, Renato Ferreira Pinto Junior, and Ilan Kogan. 2019. RadGrad: Active learning with loss gradients.arXiv:1906.07838. Retrieved from http://arxiv.org/abs/1906.07838.

[102]

Meng Fang, Yuan Li, and Trevor Cohn. 2017. Learning how to active learn: A deep reinforcement learning approach. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’17), Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 595–605.

[103]

Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu. 2020. On the sample complexity of the linear quadratic regulator. Found. Comput. Math. 20, 4 (2020), 633–679.

Digital Library

[104]

Sébastien Racanière, Theophane Weber, et al. 2017. Imagination-augmented agents for deep reinforcement learning. In Annual Conference on Neural Information Processing Systems (NIPS’17), Isabelle Guyon, Ulrike von Luxburg, and Samy Bengio, et al (Eds.). 5690–5701.

[105]

Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee. 2018. Sample-efficient reinforcement learning with stochastic ensemble value expansion. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems (NeurIPS’18), Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett (Eds.). 8234–8244.

[106]

Vladimir Feinberg, Alvin Wan et al. 2018. Model-based value estimation for efficient model-free reinforcement learning. arXiv:0803.00101. Retrieved from http://arxiv.org/abs/1803.00101.

[107]

Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, and Sergey Levine. 2016. Continuous deep q-learning with model-based acceleration. In Proceedings of the 33nd International Conference on Machine Learning (ICML’16), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.), Vol. 48. JMLR.org, 2829–2838.

[108]

Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel. 2018. Model-ensemble trust-region policy optimization. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18). OpenReview.net.

[109]

Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, and Mauricio Reyes. 2018. Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 580–588.

[110]

Xiao-Yu Zhang, Haichao Shi, Xiaobin Zhu, and Peng Li. 2019. Active semi-supervised learning based on self-expressive correlation with generative adversarial networks. Neurocomputing 345 (2019), 103–113.

Digital Library

[111]

Miriam W. Huijser and Jan C. van Gemert. 2017. Active decision boundary annotation with deep generative models. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17). IEEE Computer Society, 5296–5305.

[112]

Toan Tran, Thanh-Toan Do, Ian D. Reid, and Gustavo Carneiro. 2019. Bayesian generative active deep learning. In Proceedings of the 36th International Conference on Machine Learning (ICML’19), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. PMLR, 6295–6304.

[113]

Xueying Shi, Qi Dou, et al. 2019. An active learning approach for reducing annotation cost in skin lesion analysis. In Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging (MLMI’19), Heung-Il Suk and Mingxia Liu et al (Eds.), Vol. 11861. Springer, 628–636.

Digital Library

[114]

Noel C. F. Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen W. Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael A. Marchetti, Harald Kittler, and Allan Halpern. 2019. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC). CoRR abs/1902.03368.

[115]

Ali Mottaghi and Serena Yeung. 2019. Adversarial representation active learning. arXiv:cs.CV/1912.09720. Retrieved from https://arxiv.org/abs/1912.09720.

[116]

Daniel Gissin and Shai Shalev-Shwartz. 2019. Discriminative active learning. arXiv:1907.06347. Retrieved from https://arxiv.org/abs/1907.06347.

[117]

Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell. 2019. Variational adversarial active learning. arXiv:1904.00370. Retrieved from http://arxiv.org/abs/1904.00370.

[118]

Kwan-Young Kim, Dongwon Park, Kwang In Kim, and Se Young Chun. 2020. Task-aware variational adversarial active learning.arxiv:2002.04709. Retrieved from https://arxiv.org/abs/2002.04709.

[119]

Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2016. DeepFool: A simple and accurate method to fool deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE Computer Society, 2574–2582.

[120]

Yassir Saquil, Kwang In Kim, and Peter M. Hall. 2018. Ranking CGANs: Subjective control over semantic image attributes. In Proceedings of the British Machine Vision Conference BMVC’18)). BMVA Press, 131.

[121]

Zhao Lei, Yi Zeng, Peng Liu, and Xiaohui Su. 2021. Active deep learning for hyperspectral image classification with uncertainty learning. IEEE Geosci. Remote Sens. Lett. (2021).

[122]

B. H. Menze, A. Jakab, and S. Bauer etc.2015. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imag. 34, 10 (2015), 1993–2024.

[123]

Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 10 (2010), 1345–1359.

Digital Library

[124]

Karl R. Weiss, Taghi M. Khoshgoftaar, and Dingding Wang. 2016. A survey of transfer learning. J. Big Data 3 (2016), 9.

[125]

Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J. Storkey. 2020. Meta-learning in neural networks: A survey. CoRR abs/2004.05439.

[126]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, et al. 2020. A comprehensive survey of neural architecture search: Challenges and solutions. CoRR abs/2006.02903.

[127]

Mahmut Kaya and Hasan Sakir Bilge. 2019. Deep metric learning: A survey. Symmetry 11, 9 (2019), 1066.

[128]

Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surv. 53, 3 (2020), 63:1–63:34.

[129]

Zhi-Hua Zhou. 2018. A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5, 1 (2018), 44–53.

Cited By

Vasanthakumari PZhu YBrettin TPartin AShukla MXia FNarykov OWeil MStevens R(2024)A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug ScreeningCancers10.3390/cancers1603053016:3(530)Online publication date: 26-Jan-2024
https://doi.org/10.3390/cancers16030530
Qi XHe XChen SHai T(2024)A framework of evolutionary optimized convolutional neural network for classification of shang and chow dynasties bronze decorative patternsPLOS ONE10.1371/journal.pone.029351719:5(e0293517)Online publication date: 14-May-2024
https://doi.org/10.1371/journal.pone.0293517
Wan ZWang ZChung CWang Z(2024)A Survey of Dataset Refinement for Problems in Computer Vision DatasetsACM Computing Surveys10.1145/362715756:7(1-34)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3627157
Show More Cited By

Index Terms

A Survey on Active Deep Learning: From Model Driven to Data Driven
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

An Online Business Process Model-driven Generator of the Conceptual Database Model
WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

The paper presents an online two-phase business process model-driven generator of the conceptual database model. The generator is implemented as a web-based, platform-independent tool, in contrast to the existing tools that are dependent on some ...
Model Driven Validation of System Architectures
HASE '11: Proceedings of the 2011 IEEE 13th International Symposium on High-Assurance Systems Engineering

The architecture is the basic structure of every system. The system architect is responsible for ensuring that it fits to the system requirements even if these requirements change according to new conditions during development process. Our approach ...
Model-driven engineering

During the last decade a new trend of approaches has emerged, which considers models not just documentation artefacts, but also central artefacts in the software engineering field, allowing the creation or automatic execution of software systems ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 10s

January 2022

831 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551649

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2022

Online AM: 23 March 2022

Accepted: 03 January 2022

Revised: 27 November 2021

Received: 29 May 2021

Published in CSUR Volume 54, Issue 10s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Funding Sources

NSFC

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

58
Total Citations
View Citations
3,247
Total Downloads

Downloads (Last 12 months)1,431
Downloads (Last 6 weeks)88

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vasanthakumari PZhu YBrettin TPartin AShukla MXia FNarykov OWeil MStevens R(2024)A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug ScreeningCancers10.3390/cancers1603053016:3(530)Online publication date: 26-Jan-2024
https://doi.org/10.3390/cancers16030530
Qi XHe XChen SHai T(2024)A framework of evolutionary optimized convolutional neural network for classification of shang and chow dynasties bronze decorative patternsPLOS ONE10.1371/journal.pone.029351719:5(e0293517)Online publication date: 14-May-2024
https://doi.org/10.1371/journal.pone.0293517
Wan ZWang ZChung CWang Z(2024)A Survey of Dataset Refinement for Problems in Computer Vision DatasetsACM Computing Surveys10.1145/362715756:7(1-34)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3627157
Chowdhury SHamerly GMcGarrity M(2024)Active Learning Strategy Using Contrastive Learning and K-means for Aquatic Invasive Species Recognition2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW60836.2024.00097(848-858)Online publication date: 1-Jan-2024
https://doi.org/10.1109/WACVW60836.2024.00097
He KZhang ZDong YCai DLu YHan W(2024)Improving Geological Remote Sensing Interpretation Via a Contextually Enhanced Multiscale Feature Fusion NetworkIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.337481817(6158-6173)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3374818
Lee SKoganti SHwang IWoo M(2024)Class Probability-guided Ensemble Learning-based Semantic Segmentation to Predict Cancerous Regions on Hematoxylin and Eosin-stained Images2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00014(49-56)Online publication date: 5-Feb-2024
https://doi.org/10.1109/ICSC59802.2024.00014
Wang MLiu GLiu H(2024)Object Detection via Active Learning Strategy Based on Saliency of Local Features and Posterior ProbabilityIEEE Access10.1109/ACCESS.2024.337259512(35462-35474)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3372595
Dong HBarnard AParker A(2024)Online meta-learned gradient norms for active learning in science and technologyMachine Learning: Science and Technology10.1088/2632-2153/ad2e175:1(015041)Online publication date: 8-Mar-2024
https://doi.org/10.1088/2632-2153/ad2e17
Liu MLiu PZhao LMa YChen LXu M(2024)Fast semantic segmentation for remote sensing images with an improved Short-Term Dense-Connection (STDC) networkInternational Journal of Digital Earth10.1080/17538947.2024.235612217:1Online publication date: 3-Jun-2024
https://doi.org/10.1080/17538947.2024.2356122
Wang HChen BYe QHuang F(2024)MREDNet: A Multi-Residual Encoder-Decoder Network for recovering LFM interfered by intra-pulse retransmission interferencesSignal Processing10.1016/j.sigpro.2023.109329217(109329)Online publication date: May-2024
https://doi.org/10.1016/j.sigpro.2023.109329
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents