CoChat
Sign Up
Cited by 1,169Machine Learning and AlgorithmsDomain Adaptation and Few-Shot LearningMachine Learning and Data Classification

The DOI matches its Crossref record; 151 of the 152 references it cites were found in a registry or an index.

A Survey of Deep Active Learning

Open access
Ren, Pengzhen, Xiao, Yun, Chang, Xiaojun, Huang, Po-Yao, Li, Zhihui, Gupta, Brij B., Chen, Xiaojiang, Wang, Xin·2022·ACM Computing Surveys
DOI 10.1145/3472291
Open this paper in CoChat — free accountOpen PDF

Abstract

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.

Overview

Integrity signals

Registry

Crossref · 10.1145/3472291

Cites

151 of 151 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

1 of the 152 references couldn’t be matched to a known paper.

Research works printed but not linked: 109

  1. Mohammed Abdel-Wahab and Carlos Busso . 2019 . Active learning for speech emotion recognition using deep neural network . In Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction. IEEE, 1–7. Mohammed Abdel-Wahab and Carlos Busso. 2019. Active learning for speech emotion recognition using deep neural network. In Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction. IEEE, 1–7.
  2. Charu C. Aggarwal , Xiangnan Kong , Quanquan Gu , Jiawei Han , and Philip S . Yu . 2014 . Active learning: A survey. In Data Classification: Algorithms and Applications. CRC Press , 571–606. Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S. Yu. 2014. Active learning: A survey. In Data Classification: Algorithms and Applications. CRC Press, 571–606.
  3. Saeed S. Alahmari , Dmitry B. Goldgof , Lawrence O. Hall , and Peter R. Mouton . 2019. Automatic cell counting using active deep learning and unbiased stereology . In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 1708–1713 . Saeed S. Alahmari, Dmitry B. Goldgof, Lawrence O. Hall, and Peter R. Mouton. 2019. Automatic cell counting using active deep learning and unbiased stereology. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 1708–1713.
  4. Bang An , Wenjun Wu , and Huimin Han . 2018 . Deep active learning for text classification. In Proceedings of the 2nd International Conference on Vision , Image and Signal Processing. ACM , 22 : 1 – 22 :6. Bang An, Wenjun Wu, and Huimin Han. 2018. Deep active learning for text classification. In Proceedings of the 2nd International Conference on Vision, Image and Signal Processing. ACM, 22:1–22:6.
  5. Olov Andersson , Mariusz Wzorek , and Patrick Doherty . 2017 . Deep learning quadcopter control via risk-aware active learning . In Proceedings of the 31st AAAI Conference on Artificial Intelligence , February 4-9, 2017, San Francisco, California. AAAI Press, 3812–3818. Olov Andersson, Mariusz Wzorek, and Patrick Doherty. 2017. Deep learning quadcopter control via risk-aware active learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California. AAAI Press, 3812–3818.
  6. Jordan T. Ash , Chicheng Zhang , Akshay Krishnamurthy , John Langford , and Alekh Agarwal . 2020 . Deep batch active learning by diverse, uncertain gradient lower bounds . In Proceedings of the 8th International Conference on Learning Representations. Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020. Deep batch active learning by diverse, uncertain gradient lower bounds. In Proceedings of the 8th International Conference on Learning Representations.
  7. Anthony Bau , Yonatan Belinkov , Hassan Sajjad , Nadir Durrani , Fahim Dalvi , and James R. Glass . 2019. Identifying and controlling important neurons in neural machine translation . In Proceedings of the 7th International Conference on Learning Representations. Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James R. Glass. 2019. Identifying and controlling important neurons in neural machine translation. In Proceedings of the 7th International Conference on Learning Representations.
  8. 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 2018 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 9368–9377 . 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 2018 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 9368–9377.
  9. 10.5555/2976456.2976476 · DOI
  10. Mustafa Bilgic and Lise Getoor . 2009 . Link-based active learning . In Proceedings of the NIPS Workshop on Analyzing Networks and Learning with Graphs. Mustafa Bilgic and Lise Getoor. 2009. Link-based active learning. In Proceedings of the NIPS Workshop on Analyzing Networks and Learning with Graphs.
  11. John Blitzer , Mark Dredze , and Fernando Pereira . 2007 . Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification . In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. The Association for Computational Linguistics. John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. The Association for Computational Linguistics.
  12. Michael Bloodgood. 2010. Chris Callison-Burch: Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation. ACL 854–864. Michael Bloodgood. 2010. Chris Callison-Burch: Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation. ACL 854–864.
  13. Michael Bloodgood and K. Vijay-Shanker . 2009. Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets . In Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics. The Association for Computational Linguistics, 137–140 . Michael Bloodgood and K. Vijay-Shanker. 2009. Taking into account the differences between actively and passively acquired data: The case of active learning with support vector machines for imbalanced datasets. In Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics. The Association for Computational Linguistics, 137–140.
  14. Erik Bochinski , Ghassen Bacha , Volker Eiselein , Tim J. W. Walles , Jens C. Nejstgaard , and Thomas Sikora . 2018. Deep active learning for in situ plankton classification . In Proceedings of the Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNAZ . Zhang, D. Suter, Y. Tian, A. Branzan Albu, N. Sidére, H. Jair Escalante (Eds.), Lecture Notes in Computer Science, Vol. 11188 . Springer , 5–15. Erik Bochinski, Ghassen Bacha, Volker Eiselein, Tim J. W. Walles, Jens C. Nejstgaard, and Thomas Sikora. 2018. Deep active learning for in situ plankton classification. In Proceedings of the Pattern Recognition and Information Forensics - ICPR 2018 International Workshops, CVAUI, IWCF, and MIPPSNAZ. Zhang, D. Suter, Y. Tian, A. Branzan Albu, N. Sidére, H. Jair Escalante (Eds.), Lecture Notes in Computer Science, Vol. 11188. Springer, 5–15.
  15. Klaus Brinker . 2003 . Incorporating diversity in active learning with support vector machines . In Proceedings of the 20th International Conference on Machine Learning. AAAI Press, 59–66 . Klaus Brinker. 2003. Incorporating diversity in active learning with support vector machines. In Proceedings of the 20th International Conference on Machine Learning. AAAI Press, 59–66.
  16. Alex Burka and Katherine J . Kuchenbecker . 2017 . How much haptic surface data is enough? In Proceedings of the 2017 AAAI Spring Symposia. AAAI Press . Alex Burka and Katherine J. Kuchenbecker. 2017. How much haptic surface data is enough? In Proceedings of the 2017 AAAI Spring Symposia. AAAI Press.
  17. Kashyap Chitta Jose M. Alvarez Elmar Haussmann and Clement Farabet. 2019. Training data distribution search with ensemble active learning. arXiv:1905.12737. Retrieved from https://arxiv.org/abs/1905.12737. Kashyap Chitta Jose M. Alvarez Elmar Haussmann and Clement Farabet. 2019. Training data distribution search with ensemble active learning. arXiv:1905.12737. Retrieved from https://arxiv.org/abs/1905.12737. · arXiv
  18. Kashyap Chitta Jose M. Alvarez and Adam Lesnikowski. 2018. Large-scale visual active learning with deep probabilistic ensembles. arXiv:1811.03575. Retrieved from https://arxiv.org/abs/1811.03575. Kashyap Chitta Jose M. Alvarez and Adam Lesnikowski. 2018. Large-scale visual active learning with deep probabilistic ensembles. arXiv:1811.03575. Retrieved from https://arxiv.org/abs/1811.03575. · arXiv
  19. Ido Dagan and Sean P. Engelson . 1995. Committee-based sampling for training probabilistic classifiers . In Proceedings of the 12th International Conference on Machine Learning, 150–157 . Ido Dagan and Sean P. Engelson. 1995. Committee-based sampling for training probabilistic classifiers. In Proceedings of the 12th International Conference on Machine Learning, 150–157.
  20. Cristian Danescu-Niculescu-Mizil and Lillian Lee . 2011 . Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs . In Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics. Association for Computational Linguistics, 76–87 . Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics. Association for Computational Linguistics, 76–87.
  21. Sajib Dasgupta and Vincent Ng. 2009. Mine the easy, classify the hard: A semi-supervised approach to automatic sentiment classification . In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLPKeh-Yih Su , Jian Su, and Janyce Wiebe (Eds.). The Association for Computer Linguistics , 701–709. Retrieved from https://www.aclweb.org/anthology/P09-1079/. Sajib Dasgupta and Vincent Ng. 2009. Mine the easy, classify the hard: A semi-supervised approach to automatic sentiment classification. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLPKeh-Yih Su, Jian Su, and Janyce Wiebe (Eds.). The Association for Computer Linguistics, 701–709. Retrieved from https://www.aclweb.org/anthology/P09-1079/.
  22. Jia Deng , Alex Berg , Sanjeev Satheesh , Hao Su , Aditya Khosla , and Fei-Fei Li . 2012 . Large scale visual recognition challenge . Retrieved August 25, 2021 from www. image-net. org/challenges/LSVRC/2012. 1 (2012). Jia Deng, Alex Berg, Sanjeev Satheesh, Hao Su, Aditya Khosla, and Fei-Fei Li. 2012. Large scale visual recognition challenge. Retrieved August 25, 2021 from www. image-net. org/challenges/LSVRC/2012. 1 (2012).
  23. 10.5555/2772763.2772800 · DOI
  24. Jeff Donahue and Karen Simonyan . 2019 . Large scale adversarial representation learning . In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 10541–10551. Jeff Donahue and Karen Simonyan. 2019. Large scale adversarial representation learning. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 10541–10551.
  25. David L. Donoho . 2000 . High-dimensional data analysis: The curses and blessings of dimensionality . AMS Math Challenges Lecture 1 , 2000 (2000), 32 . David L. Donoho. 2000. High-dimensional data analysis: The curses and blessings of dimensionality. AMS Math Challenges Lecture 1, 2000 (2000), 32.
  26. Melanie Ducoffe and Frédéric Precioso. 2018. Adversarial active learning for deep networks: A margin based approach. arXiv:1802.09841. Retrieved from https://arxiv.org/abs/1802.09841. Melanie Ducoffe and Frédéric Precioso. 2018. Adversarial active learning for deep networks: A margin based approach. arXiv:1802.09841. Retrieved from https://arxiv.org/abs/1802.09841. · arXiv
  27. Rosa L. Figueroa , Qing Zeng-Treitler , Long H. Ngo , Sergey Goryachev , and Eduardo P . Wiechmann . 2012 . Active learning for clinical text classification: Is it better than random sampling?Journal of the American Medical Informatics Association 19, 5 (2012), 809–816. Rosa L. Figueroa, Qing Zeng-Treitler, Long H. Ngo, Sergey Goryachev, and Eduardo P. Wiechmann. 2012. Active learning for clinical text classification: Is it better than random sampling?Journal of the American Medical Informatics Association 19, 5 (2012), 809–816.
  28. Frederic Brenton Fitch . 1944. McCulloch warren s. and pitts walter. a logical calculus of the ideas immanent in nervous activity. bulletin of mathematical biophysics , vol. 5 ( 1943 ), pp. 115– 133 .Journal of Symbolic Logic 9, 2 (1944), 49–50. Frederic Brenton Fitch. 1944. McCulloch warren s. and pitts walter. a logical calculus of the ideas immanent in nervous activity. bulletin of mathematical biophysics, vol. 5 (1943), pp. 115–133.Journal of Symbolic Logic 9, 2 (1944), 49–50.
  29. Tavis Forrester , William J. McShea , R. W. Keys , Robert Costello , Megan Baker , and Arielle Parsons . 2013 . eMammal–citizen science camera trapping as a solution for broad-scale, long-term monitoring of wildlife populations . In Proceedings of the 98th ESA Annual Convention 2013 Sustainable Pathways: Learning from the Past and Shaping the Future (2013). Tavis Forrester, William J. McShea, R. W. Keys, Robert Costello, Megan Baker, and Arielle Parsons. 2013. eMammal–citizen science camera trapping as a solution for broad-scale, long-term monitoring of wildlife populations. In Proceedings of the 98th ESA Annual Convention 2013 Sustainable Pathways: Learning from the Past and Shaping the Future (2013).
  30. Alexander Freytag , Erik Rodner , and Joachim Denzler . 2014. Selecting influential examples: Active learning with expected model output changes . In Proceedings of the Computer Vision - ECCV 2014-13th European Conference .D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science, Vol. 8692 . Springer , 562–577. Alexander Freytag, Erik Rodner, and Joachim Denzler. 2014. Selecting influential examples: Active learning with expected model output changes. In Proceedings of the Computer Vision - ECCV 2014-13th European Conference.D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science, Vol. 8692. Springer, 562–577.
  31. Yarin Gal and Zoubin Ghahramani. 2015. Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv:1506.02158. Retrieved from https://arxiv.org/abs/1506.02158. Yarin Gal and Zoubin Ghahramani. 2015. Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv:1506.02158. Retrieved from https://arxiv.org/abs/1506.02158. · arXiv
  32. Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In Proceedings of the 33rd International Conference on Machine Learning.1050–1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the 33rd International Conference on Machine Learning.1050–1059.
  33. Yarin Gal , Riashat Islam , and Zoubin Ghahramani . 2017 . Deep bayesian active learning with image data . In Proceedings of the 34th International Conference on Machine Learning. 1183–1192 . Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017. Deep bayesian active learning with image data. In Proceedings of the 34th International Conference on Machine Learning. 1183–1192.
  34. Utkarsh Gaur , Matthew Kourakis , Erin Newman-Smith , William Smith , and B. S. Manjunath . 2016. Membrane segmentation via active learning with deep networks . In Proceedings of the 2016 IEEE International Conference on Image Processing. IEEE , 1943–1947. Utkarsh Gaur, Matthew Kourakis, Erin Newman-Smith, William Smith, and B. S. Manjunath. 2016. Membrane segmentation via active learning with deep networks. In Proceedings of the 2016 IEEE International Conference on Image Processing. IEEE, 1943–1947.
  35. Yonatan Geifman and Ran El-Yaniv. 2017. Deep active learning over the long tail. arXiv:1711.00941. Retrieved from https://arxiv.org/abs/1711.00941. Yonatan Geifman and Ran El-Yaniv. 2017. Deep active learning over the long tail. arXiv:1711.00941. Retrieved from https://arxiv.org/abs/1711.00941. · arXiv
  36. Yonatan Geifman and Ran El-Yaniv . 2019 . Deep active learning with a neural architecture search . In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 5974–5984. Yonatan Geifman and Ran El-Yaniv. 2019. Deep active learning with a neural architecture search. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 5974–5984.
  37. Daniel Gissin and Shai Shalev-Shwartz. 2019. Discriminative active learning. arXiv:1907.06347. Retrieved from https://arxiv.org/abs/1907.06347. Daniel Gissin and Shai Shalev-Shwartz. 2019. Discriminative active learning. arXiv:1907.06347. Retrieved from https://arxiv.org/abs/1907.06347. · arXiv
  38. Ian J. Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron C. Courville , and Yoshua Bengio . 2014 . Generative adversarial nets . In Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014. 2672–2680. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014. 2672–2680.
  39. Gautham Krishna Gudur Prahalathan Sundaramoorthy and Venkatesh Umaashankar. 2019. ActiveHARNet: Towards on-device deep bayesian active learning for human activity recognition. arXiv:1906.00108. Retrieved from https://arxiv.org/abs/1906.00108. Gautham Krishna Gudur Prahalathan Sundaramoorthy and Venkatesh Umaashankar. 2019. ActiveHARNet: Towards on-device deep bayesian active learning for human activity recognition. arXiv:1906.00108. Retrieved from https://arxiv.org/abs/1906.00108. · arXiv
  40. Yuhong Guo . 2010 . Active instance sampling via matrix partition . In Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010.802–810. Yuhong Guo. 2010. Active instance sampling via matrix partition. In Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010.802–810.
  41. Geoffrey E. Hinton Nitish Srivastava Alex Krizhevsky Ilya Sutskever and Ruslan Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580. Retrieved from https://arxiv.org/abs/1207.0580. Geoffrey E. Hinton Nitish Srivastava Alex Krizhevsky Ilya Sutskever and Ruslan Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580. Retrieved from https://arxiv.org/abs/1207.0580. · arXiv
  42. H. M. Sajjad Hossain , M. D. Abdullah Al Haiz Khan, and Nirmalya Roy. 2018. DeActive: Scaling activity recognition with active deep learning . In Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies 2 , 2 ( 2018 ), 66:1–66:23. H. M. Sajjad Hossain, M. D. Abdullah Al Haiz Khan, and Nirmalya Roy. 2018. DeActive: Scaling activity recognition with active deep learning. In Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies 2, 2 (2018), 66:1–66:23.
  43. Rania Ibrahim , Noha A. Yousri , Mohamed A. Ismail , and Nagwa M . El-Makky. 2014. Multi-level gene/MiRNA feature selection using deep belief nets and active learning . In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 3957–3960 . Rania Ibrahim, Noha A. Yousri, Mohamed A. Ismail, and Nagwa M. El-Makky. 2014. Multi-level gene/MiRNA feature selection using deep belief nets and active learning. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 3957–3960.
  44. David Janz Jos van der Westhuizen and José Miguel Hernández-Lobato. 2017. Actively learning what makes a discrete sequence valid. arXiv:1708.04465. Retrieved from https://arxiv.org/abs/1708.04465. David Janz Jos van der Westhuizen and José Miguel Hernández-Lobato. 2017. Actively learning what makes a discrete sequence valid. arXiv:1708.04465. Retrieved from https://arxiv.org/abs/1708.04465. · arXiv
  45. Khaled Jedoui Ranjay Krishna Michael Bernstein and Fei-Fei Li. 2019. Deep bayesian active learning for multiple correct outputs. arXiv:1912.01119. Retrieved from https://arxiv.org/abs/1912.01119. Khaled Jedoui Ranjay Krishna Michael Bernstein and Fei-Fei Li. 2019. Deep bayesian active learning for multiple correct outputs. arXiv:1912.01119. Retrieved from https://arxiv.org/abs/1912.01119. · arXiv
  46. Kwanyoung 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/abs2002.04709. Kwanyoung 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/abs2002.04709. · arXiv
  47. Ross D. King , Kenneth E. Whelan , Ffion M. Jones , Philip G. K. Reiser , Christopher H. Bryant , Stephen Muggleton , Douglas B. Kell , and Stephen G . Oliver . 2004 . Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 6971 (2004), 247–252. Ross D. King, Kenneth E. Whelan, Ffion M. Jones, Philip G. K. Reiser, Christopher H. Bryant, Stephen Muggleton, Douglas B. Kell, and Stephen G. Oliver. 2004. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 6971 (2004), 247–252.
  48. Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes . In Proceedings of the 2nd International Conference on Learning Representations. Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational bayes. In Proceedings of the 2nd International Conference on Learning Representations.
  49. Andreas Kirsch , Joost van Amersfoort , and Yarin Gal . 2019 . BatchBALD: Efficient and diverse batch acquisition for deep bayesian active learning . In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 7024–7035. Andreas Kirsch, Joost van Amersfoort, and Yarin Gal. 2019. BatchBALD: Efficient and diverse batch acquisition for deep bayesian active learning. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 7024–7035.
  50. Xiangnan Kong , Jiawei Zhang , and Philip S. Yu . 2013. Inferring anchor links across multiple heterogeneous social networks . In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. ACM, 179–188 . Xiangnan Kong, Jiawei Zhang, and Philip S. Yu. 2013. Inferring anchor links across multiple heterogeneous social networks. In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. ACM, 179–188.
  51. M. P. Kumar , Benjamin Packer , and Daphne Koller . 2010 . Self-paced learning for latent variable models . In Proceedings of the Advances in Neural Information Processing Systems (2010), 1189–1197. M. P. Kumar, Benjamin Packer, and Daphne Koller. 2010. Self-paced learning for latent variable models. In Proceedings of the Advances in Neural Information Processing Systems (2010), 1189–1197.
  52. Yann Lecun , Yoshua Bengio , and Geoffrey E Hinton . 2015. Deep learning. Nature 521, 7553 ( 2015 ), 436–444. Yann Lecun, Yoshua Bengio, and Geoffrey E Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.
  53. Xiao Lin and Devi Parikh. 2017. Active learning for visual question answering: An empirical study. arXiv:1711.01732. Retrieved from https://arxiv.org/abs1711.01732. Xiao Lin and Devi Parikh. 2017. Active learning for visual question answering: An empirical study. arXiv:1711.01732. Retrieved from https://arxiv.org/abs1711.01732. · arXiv
  54. R. G. Mark , P. S. Schluter , G. Moody , P. Devlin , and D. Chernoff . 1982 . An annotated ECG database for evaluating arrhythmia detectors . In Proceedings of the IEEE Transactions on Biomedical Engineering , Vol. 29. 600 – 600 . R. G. Mark, P. S. Schluter, G. Moody, P. Devlin, and D. Chernoff. 1982. An annotated ECG database for evaluating arrhythmia detectors. In Proceedings of the IEEE Transactions on Biomedical Engineering, Vol. 29. 600–600.
  55. Giovanna Martínez-Arellano and Svetan M. Ratchev . 2019. Towards an active learning approach to tool condition monitoring with bayesian deep learning . In Proceedings of the 33rd International ECMS Conference on Modelling and Simulation. 223–229 . Giovanna Martínez-Arellano and Svetan M. Ratchev. 2019. Towards an active learning approach to tool condition monitoring with bayesian deep learning. In Proceedings of the 33rd International ECMS Conference on Modelling and Simulation. 223–229.
  56. Ali Mottaghi and Serena Yeung. 2019. Adversarial representation active learning. arXiv:1912.09720. Retrieved from https://arxiv.org/abs1912.09720. Ali Mottaghi and Serena Yeung. 2019. Adversarial representation active learning. arXiv:1912.09720. Retrieved from https://arxiv.org/abs1912.09720. · arXiv
  57. Prateek Munjal Nasir Hayat Munawar Hayat Jamshid Sourati and Shadab Khan. 2020. Towards robust and reproducible active learning using neural networks. arXiv:2002.09564. Retrieved from https://arxiv.org/abs2002.09564. Prateek Munjal Nasir Hayat Munawar Hayat Jamshid Sourati and Shadab Khan. 2020. Towards robust and reproducible active learning using neural networks. arXiv:2002.09564. Retrieved from https://arxiv.org/abs2002.09564. · arXiv
  58. T. Hieu Nguyen and Arnold Smeulders . 2004 . Active learning using pre-clustering . In Proceedings of the 21st International Conference on Machine Learning, 79–79 . T. Hieu Nguyen and Arnold Smeulders. 2004. Active learning using pre-clustering. In Proceedings of the 21st International Conference on Machine Learning, 79–79.
  59. Mohammad Sadegh Norouzzadeh Dan Morris Sara Beery Neel Joshi Nebojsa Jojic and Jeff Clune. 2019. A deep active learning system for species identification and counting in camera trap images. arXiv:1910.09716 Retrieved from https://arxiv.org/abs1910.09716. Mohammad Sadegh Norouzzadeh Dan Morris Sara Beery Neel Joshi Nebojsa Jojic and Jeff Clune. 2019. A deep active learning system for species identification and counting in camera trap images. arXiv:1910.09716 Retrieved from https://arxiv.org/abs1910.09716. · arXiv
  60. Augustus Odena , Christopher Olah , and Jonathon Shlens . 2017 . Conditional image synthesis with auxiliary classifier GANs . In Proceedings of the 34th International Conference on Machine Learning , Vol. 70 , 2642–2651. Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the 34th International Conference on Machine Learning, Vol. 70, 2642–2651.
  61. Daniel S. Park , William Chan , Yu Zhang , Chung-Cheng Chiu , Barret Zoph , Ekin D. Cubuk , and Quoc V . Le . 2019 . SpecAugment: A simple data augmentation method for automatic speech recognition. In Proceedings of the 20th Annual Conference of the International Speech Communication Association . 2613–2617. Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le. 2019. SpecAugment: A simple data augmentation method for automatic speech recognition. In Proceedings of the 20th Annual Conference of the International Speech Communication Association. 2613–2617.
  62. 10.5555/1572392.1572411 · DOI
  63. Jeff M. Phillips. 2016. Coresets and sketches. arXiv:1601.00617. Retrieved from https://arxiv.org/abs1601.00617. Jeff M. Phillips. 2016. Coresets and sketches. arXiv:1601.00617. Retrieved from https://arxiv.org/abs1601.00617. · arXiv
  64. Robert Pinsler Jonathan Gordon Eric T. Nalisnick and José Miguel Hernández-Lobato. 2019. Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS 6356–6367. Robert Pinsler Jonathan Gordon Eric T. Nalisnick and José Miguel Hernández-Lobato. 2019. Bayesian Batch Active Learning as Sparse Subset Approximation. NeurIPS 6356–6367.
  65. Emmanouil Antonios Platanios , Mrinmaya Sachan , Graham Neubig , and Tom M. Mitchell . 2018. Contextual parameter generation for universal neural machine translation . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 425–435 . Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, and Tom M. Mitchell. 2018. Contextual parameter generation for universal neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 425–435.
  66. Remus Pop and Patric Fulop. 2018. Deep ensemble bayesian active learning: Addressing the mode collapse issue in monte carlo dropout via ensembles. arXiv:1811.03897. Retrieved from https://arxiv.org/abs1811.03897. Remus Pop and Patric Fulop. 2018. Deep ensemble bayesian active learning: Addressing the mode collapse issue in monte carlo dropout via ensembles. arXiv:1811.03897. Retrieved from https://arxiv.org/abs1811.03897. · arXiv
  67. Ameya Prabhu , Charles Dognin , and Maneesh Singh . 2019. Sampling bias in deep active classification: An empirical study . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing . Association for Computational Linguistics , 4056–4066. Ameya Prabhu, Charles Dognin, and Maneesh Singh. 2019. Sampling bias in deep active classification: An empirical study. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 4056–4066.
  68. Sameer Pradhan , Alessandro Moschitti , Nianwen Xue , Hwee Tou Ng , Anders Björkelund , Olga Uryupina , Yuchen Zhang , and Zhi Zhong . 2013 . Towards robust linguistic analysis using ontonotes . In Proceedings of the 17th Conference on Computational Natural Language Learning. ACL, 143–152 . Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using ontonotes. In Proceedings of the 17th Conference on Computational Natural Language Learning. ACL, 143–152.
  69. Yao Qin Nicholas Carlini Garrison W. Cottrell Ian J. Goodfellow and Colin Raffel. 2019. Imperceptible Robust and Targeted Adversarial Examples for Automatic Speech Recognition. ICML 5231–5240. Yao Qin Nicholas Carlini Garrison W. Cottrell Ian J. Goodfellow and Colin Raffel. 2019. Imperceptible Robust and Targeted Adversarial Examples for Automatic Speech Recognition. ICML 5231–5240.
  70. Zhenshen Qu Jingda Du Yong Cao Qiuyu Guan and Pengbo Zhao. 2020. Deep active learning for remote sensing object detection. arXiv:2003.08793. Retrieved from https://arxiv.org/abs2003.08793. Zhenshen Qu Jingda Du Yong Cao Qiuyu Guan and Pengbo Zhao. 2020. Deep active learning for remote sensing object detection. arXiv:2003.08793. Retrieved from https://arxiv.org/abs2003.08793. · arXiv
  71. Nicholas Roy and Andrew McCallum . 2001 . Toward optimal active learning through monte carlo estimation of error reduction . In Proceedings of the International Conference on Machine Learning, 441–448 . Nicholas Roy and Andrew McCallum. 2001. Toward optimal active learning through monte carlo estimation of error reduction. In Proceedings of the International Conference on Machine Learning, 441–448.
  72. David E. Rumelhart , Geoffrey E. Hinton , and Ronald J . Williams . 1986 . Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1 . MIT Press . 318–362. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1986. Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. MIT Press. 318–362.
  73. Mathew Salvaris Danielle Dean and Wee Hyong Tok. 2018. Generative adversarial networks. arXiv:1406.2661. Retrieved from https://arxiv.org/abs1406.2661. Mathew Salvaris Danielle Dean and Wee Hyong Tok. 2018. Generative adversarial networks. arXiv:1406.2661. Retrieved from https://arxiv.org/abs1406.2661. · arXiv
  74. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition . In Proceedings of the 7th Conference on Natural Language Learning. ACL, 142–147 . Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the 7th Conference on Natural Language Learning. ACL, 142–147.
  75. Yassir Saquil , Kwang In Kim , and Peter Hall . 2018 . Ranking CGANs: Subjective control over semantic image attributes . In Proceedings of the British Machine Vision Conference 2018. BMVA Press, 131. Yassir Saquil, Kwang In Kim, and Peter Hall. 2018. Ranking CGANs: Subjective control over semantic image attributes. In Proceedings of the British Machine Vision Conference 2018. BMVA Press, 131.
  76. Melanie Lubrano Di Scandalea Christian S. Perone Mathieu Boudreau and Julien Cohenadad. 2019. Deep active learning for axon-myelin segmentation on histology data. arXiv:1907.05143. Retrieved from https://arxiv.org/abs1907.05143. Melanie Lubrano Di Scandalea Christian S. Perone Mathieu Boudreau and Julien Cohenadad. 2019. Deep active learning for axon-myelin segmentation on histology data. arXiv:1907.05143. Retrieved from https://arxiv.org/abs1907.05143. · arXiv
  77. 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/abs2008.07267. 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/abs2008.07267. · arXiv
  78. Ozan Sener and Silvio Savarese. 2017. A geometric approach to active learning for convolutional neural networks. arXiv:1708.00489. Retrieved from https://arxiv.org/abs1708.00489. Ozan Sener and Silvio Savarese. 2017. A geometric approach to active learning for convolutional neural networks. arXiv:1708.00489. Retrieved from https://arxiv.org/abs1708.00489. · arXiv
  79. Ozan Sener and Silvio Savarese . 2018 . Active learning for convolutional neural networks: A core-set approach . In Proceedings of the International Conference on Learning Representations. Ozan Sener and Silvio Savarese. 2018. Active learning for convolutional neural networks: A core-set approach. In Proceedings of the International Conference on Learning Representations.
  80. Burr Settles . 2012. Active learning , volume 6 of synthesis lectures on artificial intelligence and machine learning . Synthesis Lectures on Artificial Intelligence and Machine LearningMorgan & Claypool . Burr Settles. 2012. Active learning, volume 6 of synthesis lectures on artificial intelligence and machine learning. Synthesis Lectures on Artificial Intelligence and Machine LearningMorgan & Claypool.
  81. Artem Shelmanov , Vadim Liventsev , Danil Kireev , Nikita Khromov , Alexander Panchenko , Irina Fedulova , and Dmitry V. Dylov . 2019. Active learning with deep pre-trained models for sequence tagging of clinical and biomedical texts . In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 482–489 . Artem Shelmanov, Vadim Liventsev, Danil Kireev, Nikita Khromov, Alexander Panchenko, Irina Fedulova, and Dmitry V. Dylov. 2019. Active learning with deep pre-trained models for sequence tagging of clinical and biomedical texts. In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 482–489.
  82. Changjian Shui , Fan Zhou , Christian Gagné , and Boyu Wang . 2020 . Deep active learning: Unified and principled method for query and training . In Proceedings of the International Conference on Artificial Intelligence and Statistics. 1308–1318 . Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang. 2020. Deep active learning: Unified and principled method for query and training. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 1308–1318.
  83. Aditya Siddhant and Zachary C. Lipton . 2018. Deep bayesian active learning for natural language processing: Results of a large-scale empirical study . In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2904–2909 . Aditya Siddhant and Zachary C. Lipton. 2018. Deep bayesian active learning for natural language processing: Results of a large-scale empirical study. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2904–2909.
  84. Karen Simonyan and Andrew Zisserman . 2015 . Very deep convolutional networks for large-scale image recognition . In Proceedings of the 3rd International Conference on Learning Representations. Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations.
  85. Kechen Song and Yunhui Yan . 2013. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science 285, Part B ( 2013 ), 858–864. Kechen Song and Yunhui Yan. 2013. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science 285, Part B (2013), 858–864.
  86. Akash Srivastava , Lazar Valkoz , Chris Russell , U. Michael Gutmann , and A. Charles Sutton . 2017 . VEEGAN: Reducing mode collapse in GANs using implicit variational learning. In Neural Information Processing Systems. 3310–3320. Akash Srivastava, Lazar Valkoz, Chris Russell, U. Michael Gutmann, and A. Charles Sutton. 2017. VEEGAN: Reducing mode collapse in GANs using implicit variational learning. In Neural Information Processing Systems. 3310–3320.
  87. 10.5555/2627435.2670313 · DOI
  88. Fabian Stark , Caner Hazırbas , Rudolph Triebel , and Daniel Cremers . 2015 . Captcha recognition with active deep learning . In Proceedings of the Workshop New Challenges in Neural Computation , Vol. 2015 . Citeseer, 94. Fabian Stark, Caner Hazırbas, Rudolph Triebel, and Daniel Cremers. 2015. Captcha recognition with active deep learning. In Proceedings of the Workshop New Challenges in Neural Computation, Vol. 2015. Citeseer, 94.
  89. Jörg Tiedemann . 2012 . Parallel data, tools and interfaces in OPUS . In Proceedings of the 8th International Conference on Language Resources and Evaluation. European Language Resources Association, 2214–2218 . Jörg Tiedemann. 2012. Parallel data, tools and interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation. European Language Resources Association, 2214–2218.
  90. 10.1162/153244302760185243 · DOI · Paper page
  91. 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. PMLR, 6295–6304 . 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. PMLR, 6295–6304.
  92. Toan Tran , Trung Pham , Gustavo Carneiro , Lyle Palmer , and Ian Reid . 2017 . A bayesian data augmentation approach for learning deep models . In Proceedings of the Advances in Neural Information Processing Systems. 2797–2806 . Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, and Ian Reid. 2017. A bayesian data augmentation approach for learning deep models. In Proceedings of the Advances in Neural Information Processing Systems. 2797–2806.
  93. Kailas Vodrahalli Ke Li and Jitendra Malik. 2018. Are all training examples created equal? An empirical study. arXiv:1811.12569. Retrieved from https://arxiv.org/abs1811.12569. Kailas Vodrahalli Ke Li and Jitendra Malik. 2018. Are all training examples created equal? An empirical study. arXiv:1811.12569. Retrieved from https://arxiv.org/abs1811.12569. · arXiv
  94. Menglin Wang Baisheng Lai Zhongming Jin Xiaojin Gong Jianqiang Huang and Xiansheng Hua. 2018. Deep active learning for video-based person re-identification. arXiv:1812.05785. Retrieved from https://arxiv.org/abs1812.05785. Menglin Wang Baisheng Lai Zhongming Jin Xiaojin Gong Jianqiang Huang and Xiansheng Hua. 2018. Deep active learning for video-based person re-identification. arXiv:1812.05785. Retrieved from https://arxiv.org/abs1812.05785. · arXiv
  95. Qiang Wang , Bei Li , Tong Xiao , Jingbo Zhu , Changliang Li , Derek F. Wong , and Lidia S. Chao . 2019. Learning deep transformer models for machine translation . In Proceedings of the 57th Conference of the Association for Computational Linguistics. Association for Computational Linguistics , 1810–1822. Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. 2019. Learning deep transformer models for machine translation. In Proceedings of the 57th Conference of the Association for Computational Linguistics. Association for Computational Linguistics, 1810–1822.
  96. Wenzhe Wang , Yifei Lu , Bian Wu , Tingting Chen , Danny Z. Chen , and Jian Wu. 2018. Deep active self-paced learning for accurate pulmonary nodule segmentation . In Proceedings of the 2018 21st International Conference on Medical Image Computing and Computer Assisted Intervention . Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger (Eds.), Lecture Notes in Computer Science , Vol. 11071 , 723–731. Wenzhe Wang, Yifei Lu, Bian Wu, Tingting Chen, Danny Z. Chen, and Jian Wu. 2018. Deep active self-paced learning for accurate pulmonary nodule segmentation. In Proceedings of the 2018 21st International Conference on Medical Image Computing and Computer Assisted Intervention. Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger (Eds.), Lecture Notes in Computer Science, Vol. 11071, 723–731.
  97. Max Welling and Whye Yee Teh . 2011 . Bayesian learning via stochastic gradient langevin dynamics . In Proceedings of the 28th International Conference on International Conference on Machine Learning. 681–688 . Max Welling and Whye Yee Teh. 2011. Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th International Conference on International Conference on Machine Learning. 681–688.
  98. Ismet Zeki Yalniz Hervé Jégou Kan Chen Manohar Paluri and Dhruv Kumar Mahajan. 2019. Billion-scale semi-supervised learning for image classification. arXiv:1905.00546. Retrieved from https://arxiv.org/abs1905.00546. Ismet Zeki Yalniz Hervé Jégou Kan Chen Manohar Paluri and Dhruv Kumar Mahajan. 2019. Billion-scale semi-supervised learning for image classification. arXiv:1905.00546. Retrieved from https://arxiv.org/abs1905.00546. · arXiv
  99. 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. 399–407 . 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. 399–407.
  100. Fisher Yu Haofeng Chen Xin Wang Wenqi Xian Yingying Chen Fangchen Liu Vashisht Madhavan and Trevor Darrell. 2020. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. CVPR 2633–2642. Fisher Yu Haofeng Chen Xin Wang Wenqi Xian Yingying Chen Fangchen Liu Vashisht Madhavan and Trevor Darrell. 2020. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. CVPR 2633–2642.
  101. Jiaming Zeng , Adam Lesnikowski , and Jose M . Alvarez . 2018 . The relevance of bayesian layer positioning to model uncertainty in deep bayesian active learning. arXiv:1811.12535. Retrieved from https://arxiv.org/abs1811.12535. Jiaming Zeng, Adam Lesnikowski, and Jose M. Alvarez. 2018. The relevance of bayesian layer positioning to model uncertainty in deep bayesian active learning. arXiv:1811.12535. Retrieved from https://arxiv.org/abs1811.12535. · arXiv
  102. Xiang Zhang , Junbo Jake Zhao , and Yann LeCun . 2015 . Character-level convolutional networks for text classification . In Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems. 649–657 . Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. In Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems. 649–657.
  103. Ye Zhang , Matthew Lease , and Byron C. Wallace . 2017. Active discriminative text representation learning . In Proceedings of the 31st AAAI Conference on Artificial Intelligence, 3386–3392 . Ye Zhang, Matthew Lease, and Byron C. Wallace. 2017. Active discriminative text representation learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, 3386–3392.
  104. Yizhe Zhang , Michael T. C. Ying , Lin Yang , Anil T. Ahuja , and Danny Z. Chen . 2016. Coarse-to-fine stacked fully convolutional nets for lymph node segmentation in ultrasound images . In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 443–448 . DOI:https://doi.org/10.1109/BIBM. 2016 .7822557 Yizhe Zhang, Michael T. C. Ying, Lin Yang, Anil T. Ahuja, and Danny Z. Chen. 2016. Coarse-to-fine stacked fully convolutional nets for lymph node segmentation in ultrasound images. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 443–448. DOI:https://doi.org/10.1109/BIBM.2016.7822557 · DOI
  105. Fedor Zhdanov. 2019. Diverse mini-batch active learning. arXiv:1901.05954. Retrieved from https://arxiv.org/abs1901.05954. Fedor Zhdanov. 2019. Diverse mini-batch active learning. arXiv:1901.05954. Retrieved from https://arxiv.org/abs1901.05954. · arXiv
  106. Siqi Zhou and Angela P. Schoellig . 2019. Active training trajectory generation for inverse dynamics model learning with deep neural networks . In Proceedings of the 2019 IEEE 58th Conference on Decision and Control. IEEE, 1784–1790 . Siqi Zhou and Angela P. Schoellig. 2019. Active training trajectory generation for inverse dynamics model learning with deep neural networks. In Proceedings of the 2019 IEEE 58th Conference on Decision and Control. IEEE, 1784–1790.
  107. Jia-Jie Zhu and José Bento. 2017. Generative adversarial active learning. arXiv:1702.07956. Retrieved from https://arxiv.org/abs1702.07956. Jia-Jie Zhu and José Bento. 2017. Generative adversarial active learning. arXiv:1702.07956. Retrieved from https://arxiv.org/abs1702.07956. · arXiv
  108. Michal Ziemski , Marcin Junczys-Dowmunt , and Bruno Pouliquen . 2016 . The united nations parallel corpus v1.0 . In Proceedings of the 10th International Conference on Language Resources and Evaluation. European Language Resources Association. Michal Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen. 2016. The united nations parallel corpus v1.0. In Proceedings of the 10th International Conference on Language Resources and Evaluation. European Language Resources Association.
  109. Barret Zoph and Quoc V. Le . 2017. Neural architecture search with reinforcement learning . In Proceedings of the 5th International Conference on Learning Representations. Barret Zoph and Quoc V. Le. 2017. Neural architecture search with reinforcement learning. In Proceedings of the 5th International Conference on Learning Representations.

Cited by

Sorted by citations

See all 1,169

Related

See all

Metadata

Authors
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X.
Affiliations
  • Northwest University (CN)
  • RMIT University (AU)
  • Carnegie Mellon University (US)
  • Qilu University of Technology (CN)
  • National Institute of Technology Kurukshetra (IN)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-10-08
Volume
54
Issue
9
Pages
1-40
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3472291
OpenAlex
W3205626500
Cited by
1,169
References
152
Citations by year
2026: 143 · 2025: 256 · 2024: 297 · 2023: 302 · 2022: 125 · 2021: 43
FWCI
68.03
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • RMIT Research Repository (RMIT University Library) (submittedVersion)
  • Figshare (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • NSFC (61972315 and 62072372)
  • Shaanxi Science and Technology Innovation Team Support (2018TD-026)
  • Australian Research Council Discovery Early Career Researcher Award (DE190100626)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
deep active learning, active learning (machine learning), deep learning, annotation cost reduction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review