survey

A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications

Authors:
Ms. Aayushi Bansal

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

0000-0002-3117-9459
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,
Dr. Rewa Sharma

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India
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,
Dr. Mamta Kathuria

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India
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ACM Computing Surveys Volume 54 Issue 10sArticle No.: 208pp 1–29https://doi.org/10.1145/3502287

Published:13 September 2022Publication History

ACM Computing Surveys

Abstract

Recent advancements in deep learning architecture have increased its utility in real-life applications. Deep learning models require a large amount of data to train the model. In many application domains, there is a limited set of data available for training neural networks as collecting new data is either not feasible or requires more resources such as in marketing, computer vision, and medical science. These models require a large amount of data to avoid the problem of overfitting. One of the data space solutions to the problem of limited data is data augmentation. The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network. This saves the cost and time consumption required to collect new data for the training of deep neural networks by augmenting available data. This also regularizes the model and improves its capability of generalization. The need for large datasets in different fields such as computer vision, natural language processing, security, and healthcare is also covered in this survey paper. The goal of this paper is to provide a comprehensive survey of recent advancements in data augmentation techniques and their application in various domains.

REFERENCES

Aceto G., Ciuonzo D., Montieri A., and Pescapè A.. 2019. MIMETIC: Mobile encrypted traffic classification using multimodal deep learning. Computer Networks 165, 106944. Google ScholarDigital Library
Agarwal A., Vatsa M., Singh R., and Ratha N.. 2021. Cognitive data augmentation for adversarial defense via pixel masking. Pattern Recognition Letters. Google ScholarCross Ref
Alqahtani H., Kavakli-Thorne M., and Kumar G.. 2019. Applications of generative adversarial networks (GANs): An updated review. Archives of Computational Methods in Engineering. Google ScholarCross Ref
Andresini G., Appice A., De Rose L., and Malerba D.. 2021. GAN Augmentation to Deal with Imbalance in Imaging-based Intrusion Detection 123, 108–127.Google Scholar
Apte C.. 2011. ACM Digital Library, Association for computing machinery. Special Interest Group on Knowledge Discovery & Data Mining, & Association for Computing Machinery. Special Interest Group on Management of Data. (2011). Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.Google ScholarDigital Library
Bang S., Baek F., Park S., Kim W., and Kim H.. 2020. Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques. Automation in Construction 115. Google ScholarCross Ref
Bastani O., Ioannou Y., Lampropoulos L., Vytiniotis D., Nori A. V., and Criminisi A., (n.d.). Measuring Neural Net Robustness with Constraints.Google Scholar
Bowles C., Chen L., Guerrero R., Bentley P., Hammers A., Dickie D. A., and Vald M. (n.d.). GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks.Google Scholar
Brunetti A., Buongiorno D., Trotta G. F., and Bevilacqua V.. 2018. Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing 300, 17–33. Google ScholarCross Ref
Carlini N. and Wagner D.. 2017. Towards evaluating the robustness of neural networks. Proceedings - IEEE Symposium on Security and Privacy. 39–57. Google ScholarCross Ref
Chae D. K., Kim S. W., Kang J. S., and Choi J.. 2019. Rating augmentation with generative adversarial networks towards accurate collaborative filtering. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. 2616–2622. Google ScholarDigital Library
Chaitanya K., Karani N., Baumgartner C. F., Erdil E., Becker A., Donati O., and Konukoglu E.. 2021. Semi-supervised task-driven data augmentation for medical image segmentation. Medical Image Analysis 68. Google ScholarCross Ref
Chen L., Yang H., Wu S., and Gao Z.. 2017. Data generation for improving person re-identification. MM 2017 - Proceedings of the 2017 ACM Multimedia Conference. 609–617. Google ScholarDigital Library
Cheng A.. 2019. PAC-GAN: Packet generation of network traffic using generative adversarial networks. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2019, 728–734. Google ScholarCross Ref
Choi E., Bahadori M. T., Song L., Stewart W. F., and Sun J.. 2017. GRAM: Graph-based attention model for healthcare representation learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F129685, 787–795. Google ScholarDigital Library
Costa-jussà M. R., Allauzen A., Barrault L., Cho K., and Schwenk H.. 2017. Introduction to the special issue on deep learning approaches for machine translation. Computer Speech and Language 46, 367–373. Google ScholarDigital Library
Cubuk E. D., Zoph B., Shlens J., and Le Q. V. (n.d.). Randaugment: Practical Automated Data Augmentation with a Reduced Search Space.Google Scholar
Cubuk E. D., Zoph B., Vasudevan V., and Le Google Brain Q. V. (n.d.). AutoAugment: Learning Augmentation Strategies from Data. https://pillow.readthedocs.io/en/5.1.x/.Google Scholar
Dai Y. and Wang G.. 2018. A deep inference learning framework for healthcare. Pattern Recognition Letters. Google ScholarCross Ref
Daumé H.. 2007. Frustratingly Easy Domain Adaptation. http://hal3.name/easyadapt.pl.gz.Google Scholar
Day O. and Khoshgoftaar T. M.. 2017. A survey on heterogeneous transfer learning. Journal of Big Data 4, 1 (2017) Google ScholarCross Ref
DeVries T. and Taylor G. W.. 2017a. Dataset Augmentation in Feature Space. http://arxiv.org/abs/1702.05538.Google Scholar
DeVries T. and Taylor G. W.. 2017b. Improved Regularization of Convolutional Neural Networks with Cutout. http://arxiv.org/abs/1708.04552.Google Scholar
Dhiraj and D. K. Jain. 2019. An evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery. Pattern Recognition Letters 120, 112–119. Google ScholarDigital Library
Jun Ding, Chen B., Liu H., and Huang M.. 2016. Convolutional neural network with data augmentation for SAR target recognition. IEEE Geoscience and Remote Sensing Letters 13, 3 (2016), 364–368. Google ScholarCross Ref
Ding Junhua , Li X., Kang X., and Gudivada V. N.. 2019. A case study of the augmentation and evaluation of training data for deep learning. Journal of Data and Information Quality 11, 4 (2019). Google ScholarDigital Library
Du Y., Yan Y., Chen S., and Hua Y.. 2020. Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation. Neurocomputing 384, 67–83. Google ScholarDigital Library
Duan L., Tsang I. W., and Xu D.. 2012. Domain transfer multiple kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 3 (2012), 465–479. Google ScholarDigital Library
Fayek H. M., Lech M., and Cavedon L.. 2017. Evaluating deep learning architectures for speech emotion recognition. Neural Networks 92, 60–68. Google ScholarCross Ref
Frid-Adar M., Diamant I., Klang E., Amitai M., Goldberger J., and Greenspan H.. 2018. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331. Google ScholarCross Ref
Fujiyoshi H., Hirakawa T., and Yamashita T.. 2019. Deep learning-based image recognition for autonomous driving. In IATSS Research 43, 4 (2019), 244–252. Elsevier B.V. Google ScholarCross Ref
Gatys L. A., Ecker A. S., and Bethge M.. 2016. Image style transfer using convolutional neural networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December. 2414–2423. Google ScholarCross Ref
Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., and Bengio Y.. (n.d.). Generative Adversarial Nets. http://www.github.com/goodfeli/adversarial.Google Scholar
Goodfellow I. J., Shlens J., and Szegedy C.. 2014. Explaining and Harnessing Adversarial Examples. http://arxiv.org/abs/1412.6572.Google Scholar
Guo G. and Zhang N.. 2019. A survey on deep learning based face recognition. Computer Vision and Image Understanding 189. Google ScholarDigital Library
Halevy A., Norvig P., and Pereira F.. 2009. The unreasonable effectiveness of data. IEEE Intelligent Systems 24, 2 (2009), 8–12. Google ScholarDigital Library
Han D., Liu Q., and Fan W.. 2018. A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications 95, 43–56. Google ScholarCross Ref
Han X., Liu Z., and Sun M. (n.d.). Neural Knowledge Acquisition via Mutual Attention between Knowledge Graph and Text. www.aaai.org.Google Scholar
Haralabopoulos G., Torres M. T., Anagnostopoulos I., and McAuley D.. 2021. Text data augmentations: Permutation, antonyms and negation. Expert Systems with Applications 177, (2020). Google ScholarDigital Library
Harel M. and Mannor S.. 2011. Learning from Multiple Outlooks.Google Scholar
Hidayat A. A., Purwandari K., Cenggoro T. W., and Pardamean B.. 2021. A convolutional neural network-based ancient Sundanese character classifier with data augmentation. Procedia Computer Science 179, (2020) 195–201. Google ScholarCross Ref
Ho D., Liang E., Stoica I., Abbeel P., and Chen X. (n.d.). Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. https://github.com/arcelien/pba.Google Scholar
Inoue H.. 2018. Data Augmentation by Pairing Samples for Images Classification. http://arxiv.org/abs/1801.02929.Google Scholar
Ioffe S. and Szegedy C. (n.d.). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.Google Scholar
Iqbal T. and Qureshi S.. 2020. The survey: Text generation models in deep learning. In Journal of King Saud University - Computer and Information Sciences. King Saud bin Abdulaziz University. Google ScholarCross Ref
Jackson P. T., Atapour-Abarghouei A., Bonner S., Breckon T., and Obara B. (n.d.). Style Augmentation: Data Augmentation via Style Randomization.Google Scholar
Johnson J. M. and Khoshgoftaar T. M.. 2019. Survey on deep learning with class imbalance. Journal of Big Data 6, 1 (2019) Google ScholarCross Ref
Karystinos G. N. and Pados D. A.. 2000. On overfitting, generalization, and randomly expanded training sets. In IEEE Transactions On Neural Networks (11, 5).Google Scholar
Katiyar S. and Borgohain S. K.. 2021. Image Captioning Using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation. http://arxiv.org/abs/2102.11237.Google Scholar
Khan S. H., Hayat M., Bennamoun M., Sohel F. A., and Togneri R.. 2018. Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Transactions on Neural Networks and Learning Systems 29, 8 (2018), 3573–3587. Google ScholarCross Ref
Kitchenham B. and Brereton P.. 2013. A systematic review of systematic review process research in software engineering. Information and Software Technology 55, 12 (2013), 2049–2075. Google ScholarDigital Library
Krizhevsky A., Sutskever I., and Hinton G. E. (n.d.). ImageNet Classification with Deep Convolutional Neural Networks. http://code.google.com/p/cuda-convnet/.Google Scholar
Kulis B., Saenko K., and Darrell T.. 2011. What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1785–1792. Google ScholarDigital Library
Lei C., Hu B., Wang D., Zhang S., and Chen Z.. 2019. A preliminary study on data augmentation of deep learning for image classification. ACM International Conference Proceeding Series. Google ScholarDigital Library
Lemley J., Bazrafkan S., and Corcoran P.. 2017. Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5, 5858–5869. Google ScholarCross Ref
Li F., Jialin Pan S., Jin O., Yang Q., and Zhu X.. 2012. Cross-Domain Co-Extraction of Sentiment and Topic Lexicons.Google Scholar
Li W., Duan L., Xu D., and Tsang I. W.. 2014. Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 6 (2014), 1134–1148. Google ScholarDigital Library
Lim S., Kim I., Kim T., Kim C., Brain K., and Kim S. (n.d.). Fast AutoAugment. https://github.com/kakaobrain/fast-autoaugment.Google Scholar
Liu S., Lee K., and Lee I.. 2020. Document-level multi-topic sentiment classification of Email data with BiLSTM and data augmentation. Knowledge-Based Systems 197. Google ScholarCross Ref
Liu S., Guo H., Hu J. G., Zhao X., Zhao C., Wang T., Zhu Y., Wang J., and Tang M.. 2020. A novel data augmentation scheme for pedestrian detection with attribute preserving GAN. Neurocomputing 401, 123–132. Google ScholarCross Ref
Long M., Wang J., Ding G., Pan S. J., and Yu P. S.. 2014. Adaptation regularization: A general framework for transfer learning. IEEE Transactions on Knowledge and Data Engineering 26, 5 (2014), 1076–1089. Google ScholarDigital Library
Long Y., Li Y., Zhang Q., Wei S., Ye H., and Yang J.. 2020. Acoustic data augmentation for Mandarin-English code-switching speech recognition. Applied Acoustics 161. Google ScholarCross Ref
Lu C. Y., Arcega Rustia D. J., and Lin Ta-Te. 2019. Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC-PapersOnLine 52, 30 (2019), 1–5. Google ScholarCross Ref
Ma F., Chitta R., You Q., Zhou J., Xiao H., and Gao J.. 2018. KAME: Knowledge-based attention model for diagnosis prediction in healthcare. International Conference on Information and Knowledge Management, Proceedings. 743–752. Google ScholarDigital Library
Ma F., You Q., Gao J., Zhou J., Suo Q., and Zhang A.. 2018. Risk prediction on electronic health records with prior medical knowledge. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1910–1919. Google ScholarDigital Library
Malik K. M., Krishnamurthy M., Alobaidi M., Hussain M., Alam F., and Malik G.. 2020. Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype. Expert Systems with Applications 145. Google ScholarDigital Library
Masi I., Trân A. T., Hassner T., Leksut J. T., and Medioni G.. 2016. Do we really need to collect millions of faces for effective face recognition? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9909 LNCS 579–596. Google ScholarCross Ref
Meng F., Liu H., Liang Y., Tu J., and Liu M.. 2019. Sample fusion network: An end-to-end data augmentation network for skeleton-based human action recognition. IEEE Transactions on Image Processing 28, 11 (2019), 5281–5295. Google ScholarDigital Library
Mikołajczyk A. and Grochowski M.. 2018. Data augmentation for improving deep learning in image classification problem. 2018 International Interdisciplinary PhD Workshop (IIPhDW). 117–122.Google ScholarCross Ref
Moreno-Barea F. J., Strazzera F., Jerez J. M., Urda D., and Franco L.. 2019. Forward noise adjustment scheme for data augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. 728–734. Google ScholarCross Ref
Mushtaq Z., Su S. F., and Tran Q. V.. 2021. Spectral images based environmental sound classification using CNN with meaningful data augmentation. Applied Acoustics 172, 107581. Google ScholarCross Ref
Nam J., Fu W., Kim S., Menzies T., and Tan L.. 2018. Heterogeneous defect prediction. IEEE Transactions on Software Engineering 44, 9 (2018), 874–896. Google ScholarCross Ref
Neyshabur B., Bhojanapalli S., Mcallester D., and Srebro N. (n.d.). Exploring Generalization in Deep Learning.Google Scholar
Olowookere T. A. and Adewale O. S.. 2020. A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach. Scientific African 8. Google ScholarCross Ref
Oquab M., Bottou L., Laptev I., and Sivic J.. 2014. Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1717–1724. Google ScholarDigital Library
Ornek A. H. and Ceylan M.. 2019. Comparison of traditional transformations for data augmentation in deep learning of medical thermography. 2019 42nd International Conference on Telecommunications and Signal Processing, TSP 2019, 191–194. Google ScholarCross Ref
Palatucci M., Pomerleau D., Hinton G., and Mitchell T. M. (n.d.). Zero-Shot Learning with Semantic Output Codes.Google Scholar
Pan S. J., Tsang I. W., Kwok J. T., and Yang Q.. 2011. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22, 2 (2011), 199–210. Google ScholarDigital Library
Pan S. J. and Yang Q.. 2010. A survey on transfer learning. In IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345–1359. Google ScholarDigital Library
Pan X.. 2021. Do 2D GANs Know 3D Shape? Unsupervised 3D. 1–18.Google Scholar
Pandey S., Singh P. R., and Tian J.. 2020. An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation. Biomedical Signal Processing and Control 57. Google ScholarCross Ref
Perez L. and Wang J.. 2017. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. http://arxiv.org/abs/1712.04621.Google Scholar
Prettenhofer P. and Stein B.. 2010. Cross-Language Text Classification using Structural Correspondence Learning. Association for Computational Linguistics.Google Scholar
Qian Y., Hu H., and Tan T.. 2019. Data augmentation using generative adversarial networks for robust speech recognition. Speech Communication 114, 1–9. Google ScholarDigital Library
Ratner A. J., Ehrenberg H. R., Hussain Z., Dunnmon J., and Ré C. (n.d.). Learning to Compose Domain-Specific Transformations for Data Augmentation.Google Scholar
Sajjad M., Khan S., Muhammad K., Wu W., Ullah A., and Baik S. W.. 2019. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science 30, 174–182. Google ScholarCross Ref
Salimans T., Goodfellow I., Zaremba W., Cheung V., Radford A., and Chen X.. (n.d.). Improved Techniques for Training GANs. https://github.com/openai/improved-gan.Google Scholar
Salman S. and Liu X.. 2019. Overfitting Mechanism and Avoidance in Deep Neural Networks. http://arxiv.org/abs/1901.06566.Google Scholar
Seah C. W., Ong Y. S., and Tsang I. W.. 2013. Combating negative transfer from predictive distribution differences. IEEE Transactions on Cybernetics 43, 4 (2013), 1153–1165. Google ScholarCross Ref
Sezer O. B., Gudelek M. U., and Ozbayoglu A. M.. 2020. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing Journal 90. Google ScholarCross Ref
Shakeel M. H., Karim A., and Khan I.. 2020. A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts. Information Processing and Management 57, 3 (2020). Google ScholarDigital Library
Shao L., Zhu F., and Li X.. 2015. Transfer learning for visual categorization: A survey. IEEE Transactions on Neural Networks and Learning Systems 26, 5 (2015), 1019–1034. Google Scholar
Shi X., Fan W., and Ren J. (n.d.). LNAI 5212 - Actively Transfer Domain Knowledge.Google Scholar
Shijie J. and Ping W. (n.d.). Research on Data Augmentation for Image Classification Based on Convolution Neural Networks. 201602118.Google Scholar
Shorten C. and Khoshgoftaar T. M.. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6, 1 (2019). Google ScholarCross Ref
Srivastava N., Hinton G., Krizhevsky A., and Salakhutdinov R.. 2014. Dropout: A simple way to prevent neural networks from overfitting. In Journal of Machine Learning Research (15).Google Scholar
Sultani W. and Shah M.. 2021. Human action recognition in drone videos using a few aerial training examples. Computer Vision and Image Understanding 206 (2020). Google ScholarCross Ref
Summers C. and Dinneen M. J.. 2019. Improved mixed-example data augmentation. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 1262–1270. Google ScholarCross Ref
Sun C., Shrivastava A., Singh S., and Gupta A.. 2017. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era. Google ScholarCross Ref
Szegedy C., Zaremba W., Sutskever I., Bruna J., Erhan D., Goodfellow I., and Fergus R.. 2013. Intriguing Properties of Neural Networks. http://arxiv.org/abs/1312.6199.Google Scholar
Takahashi R., Matsubara T., and Uehara K.. 2019. Data augmentation using random image cropping and patching for deep CNNs. IEEE Transactions on Circuits and Systems for Video Technology. 1–1. Google ScholarDigital Library
Taylor L. and Nitschke G.. 2019. Improving deep learning with generic data augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 1542–1547. Google ScholarCross Ref
Tommasi T., Orabona F., and Caputo B.. 2010. Safety in numbers: Learning categories from few examples with multi model knowledge transfer. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3081–3088. Google ScholarCross Ref
Waheed A., Goyal M., Gupta D., Khanna A., Al-Turjman F., and Pinheiro P. R.. 2020. CovidGAN: Data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 8, 91916–91923. Google ScholarCross Ref
Wang P., Li S., Ye F., Wang Z., and Zhang M.. 2020. PacketCGAN: Exploratory study of class imbalance for encrypted traffic classification using CGAN. IEEE International Conference on Communications, 2020-June. Google ScholarCross Ref
Wang X., Wang K., and Lian S.. 2020. A survey on face data augmentation for the training of deep neural networks. In Neural Computing and Applications. Springer. Google ScholarDigital Library
Wang X., Zhao Y., and Pourpanah F.. 2020. Recent advances in deep learning. In International Journal of Machine Learning and Cybernetics 11, 4 (2020), 747–750. Springer. Google ScholarCross Ref
Wang Y., Wei X., Tang X., Shen H., and Ding L.. 2020. CNN Tracking Based on Data Augmentation ✩. 194, 105594. Google ScholarCross Ref
Wang Y., Huang G., Song S., Pan X., Xia Y., and Wu C.. 2021. Regularizing deep networks with semantic data augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 8828(c). Google ScholarCross Ref
Weiss K., Khoshgoftaar T. M., and Wang D. D.. 2016. A survey of transfer learning. Journal of Big Data 3, 1 (2016). Google ScholarCross Ref
Wu R.. 2014. Deep Image: Scaling up Image Recognition.Google Scholar
Xia R., Zong C., Hu X., Cambria E., Jiang J., and Zhai C.. 2013. Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification. www.computer.org/intelligent.Google Scholar
Yao Y. and Doretto G.. 2010. Boosting for transfer learning with multiple sources. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1855–1862. Google ScholarCross Ref
Zaj M., Zołna K., Rostamzadeh N., and Pinheiro P. O. (n.d.). Adversarial Framing for Image and Video Classification. www.aaai.org.Google Scholar
Zajac M., Zołna K., Rostamzadeh N., and Pinheiro P. O.. 2019. Adversarial framing for image and video classification. Proceedings of the AAAI Conference on Artificial Intelligence 33, 10077–10078. Google ScholarDigital Library
Zhang J., Liu Y., Luan H., Xu J., and Sun M. (n.d.). Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization.Google Scholar
Zhang X., Wang Q., Huawei H., Zhang J., and Zhong Z. (n.d.). Adversarial Autoaugment.Google Scholar
Zhao F., Sun H., Jin L., and Jin H.. 2020. Structure-augmented knowledge graph embedding for sparse data with rule learning. Computer Communications 159, 271–278. Google ScholarCross Ref
Zhong Z., Zheng L., Kang G., Li S., and Yang Y. (n.d.). Random Erasing Data Augmentation. https://github.com/zhunzhong07/Random-Erasing.Google Scholar
Zhou J. T., Pan S. J., Tsang I. W., and Yan Y. (n.d.). Hybrid Heterogeneous Transfer Learning through Deep Learning. www.aaai.org.Google Scholar
Zhu Y., Chen Y., Lu Z., Pan S. J., Xue G.-R., Yu Y., Yang Q., and Kong H. (n.d.). Heterogeneous Transfer Learning for Image Classification. www.aaai.org.Google Scholar
Zhu Z., Huang T., Xu M., Shi B., Cheng W., and Bai X.. 2021. Progressive and Aligned Pose Attention Transfer for Person Image Generation. 1–15. http://arxiv.org/abs/2103.11622.Google Scholar
Zoph B., Ghiasi G., Lin T., Shlens J., and Le Q. V. (n.d.). Learning Data Augmentation Strategies for Object Detection.Google Scholar

Index Terms

A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. General and reference
  1. Document types
    1. Surveys and overviews

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Published in
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
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Publication History
- Published: 13 September 2022
- Online AM: 6 January 2022
- Accepted: 22 November 2021
- Revised: 21 July 2021
- Received: 2 September 2020
Published in csur Volume 54, Issue 10s

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A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications

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