Advancements in Data Augmentation and Transfer Learning:
A Comprehensive Survey to Address Data Scarcity Challenges

Salma      Fayaz; Syed Zubair      Ahmad Shah; Nusrat Mohi      ud din; Naillah      Gul; Assif      Assad
doi:10.2174/0126662558286875231215054324
Abstract

Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms.
Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.
Graphical Abstract

[1]
A. Krizhevsky, I. Sutskever,  and G.E. Hinton, "Imagenet classification with deep convolutional neural networks", Adv. Neural. Inf. Process Syst., vol. 25, 2012.
[2]
M. Huisman, J.N. van Rijn,  and A. Plaat, "A survey of deep meta-learning", Artif. Intell. Rev., vol. 54, no. 6, pp. 4483-4541, 2021.
 [http://dx.doi.org/10.1007/s10462-021-10004-4]
[3]
K. Simonyan,  and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", arxiv, vol. 2014, p. 1556, 2014.
[4]
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens,  and Z. Wojna, "Rethinking the inception architecture for computer vision", In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27-30 June, 2016, pp. 2818-2826. 
 [http://dx.doi.org/10.1109/CVPR.2016.308]
[5]
A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau,  and S. Thrun, "Dermatologist-level classification of skin cancer with deep neural networks", Nature, vol. 542, no. 7639, pp. 115-118, 2017.
 [http://dx.doi.org/10.1038/nature21056] [PMID:  28117445]
[6]
Y. Shen, B. Zhou, P. Luo,  and X. Tang, "Facefeat-gan: A two-stage approach for identity-preserving face synthesis", arxiv, vol. 2018, p. 01288, 2018.
[7]
S. Ioffe,  and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", Int. Conf. Mach. Learn., pp. 448-456, 2015.
[8]
C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang,  and C. Liu, "A survey on deep transfer learning", In 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4-7 Oct, 2018, pp. 270-279. 
[9]
C. Shorten,  and T.M. Khoshgoftaar, "A survey on image data augmentation for deep learning", J. Big Data, vol. 6, no. 1, p. 60, 2019.
 [http://dx.doi.org/10.1186/s40537-019-0197-0]
[10]
Q. Zheng, P. Zhao, Y. Li, H. Wang,  and Y. Yang, "Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification", Neural. Comput. Appl., vol. 33, no. 13, pp. 7723-7745, 2021.
 [http://dx.doi.org/10.1007/s00521-020-05514-1]
[11]
Q. Zheng, P. Zhao, D. Zhang,  and H. Wang, "MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification", Int. J. Intell. Syst., vol. 36, no. 12, pp. 7204-7238, 2021.
 [http://dx.doi.org/10.1002/int.22586]
[12]
Q. Zheng, P. Zhao, H. Wang, A. Elhanashi,  and S. Saponara, "Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation", IEEE Commun. Lett., vol. 26, no. 6, pp. 1298-1302, 2022.
 [http://dx.doi.org/10.1109/LCOMM.2022.3145647]
[13]
Q. Zheng, X. Tian, Z. Yu, N. Jiang, A. Elhanashi, S. Saponara,  and R. Yu, "Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: A case study of Qingdao, China", Sustain Cities Soc., vol. 92, p. 104486, 2023.
 [http://dx.doi.org/10.1016/j.scs.2023.104486]
[14]
Q. Zheng, X. Tian, Z. Yu, H. Wang, A. Elhanashi,  and S. Saponara, "DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization", Eng. Appl. Artif. Intell., vol. 122, p. 106082, 2023.
 [http://dx.doi.org/10.1016/j.engappai.2023.106082]
[15]
S.F. Ahmed, M.S.B. Alam, M. Hassan, M.R. Rozbu, T. Ishtiak, N. Rafa, M. Mofijur, A.B.M. Shawkat Ali,  and A.H. Gandomi, "Deep learning modelling techniques: Current progress, applications, advantages, and challenges", Artif. Intell. Rev., vol. 56, no. 11, pp. 13521-13617, 2023.
 [http://dx.doi.org/10.1007/s10462-023-10466-8] [PMID:  37362885]
[16]
Y. Chen, R. Xia, K. Yang,  and K. Zou, "GCAM: lightweight image inpainting via group convolution and attention mechanism", Int. J. Mach. Learn. Cybern., pp. 1-11, 2023.
 [http://dx.doi.org/10.1007/s13042-023-01999-z] [PMID:  37360881]
[17]
Y. Chen, R. Xia, K. Yang,  and K. Zou, "MFMAM: Image inpainting via multi-scale feature module with attention module", Comput. Vis. Image Underst., p. 103883, 2023.
[18]
Y. Chen, R. Xia, K. Yang,  and K. Zou, "DGCA: High resolution image inpainting via DR-GAN and contextual attention", Multimed Tools Appl., pp. 1-21, 2023.
[19]
Y. Chen, R. Xia, K. Yang,  and K. Zou, "DARGS: Image inpainting algorithm via deep attention residuals group and semantics", J. King Saud Univ. - Comput. Inform. Sci., vol. 35, no. 6, p. 101567, 2023.
 [http://dx.doi.org/10.1016/j.jksuci.2023.101567]
[20]
J. Pennington, R. Socher,  and C.D. Manning, "Glove: Global vectors for word representation", In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532-1543. 
 [http://dx.doi.org/10.3115/v1/D14-1162]
[21]
J. Kukačka, V. Golkov,  and D. Cremers, "Regularization for deep learning: A taxonomy", arxiv, vol. 2017, p. 10686, 2017.
[22]
S. Nitish, "Dropout: A simple way to prevent neural networks from overfitting", J. Mach. Learn. Res., vol. 15, p. 1, 2014.
[23]
K. Weiss, T.M. Khoshgoftaar,  and D. Wang, "A survey of transfer learning", J. Big Data, vol. 3, no. 1, p. 9, 2016.
 [http://dx.doi.org/10.1186/s40537-016-0043-6]
[24]
D. Erhan, A. Courville, Y. Bengio,  and P. Vincent, "Why does unsupervised pre-training help deep learning?", In Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, 2010, pp. 201-208. 
[25]
J. Deng, W. Dong, R. Socher, L-J. Li, K. Li,  and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database", In IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June, 2009. 
 [http://dx.doi.org/10.1109/CVPR.2009.5206848]
[26]
Y. Xian, C.H. Lampert, B. Schiele,  and Z. Akata, "Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly", IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 9, pp. 2251-2265, 2019.
 [http://dx.doi.org/10.1109/TPAMI.2018.2857768] [PMID:  30028691]
[27]
M. Palatucci, D. Pomerleau, G.E. Hinton,  and T.M. Mitchell, "Zero-shot learning with semantic output codes", Adv. Neural. Inf. Process Syst., vol. 22, 2009.
[28]
Y. Taigman, M. Yang, M. Ranzato,  and L. Wolf, "Deepface: Closing the gap to human-level performance in face verification", In Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, 23-28 June, 2014, pp. 1701-1708. 
 [http://dx.doi.org/10.1109/CVPR.2014.220]
[29]
G. Koch, R. Zemel,  and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition", In Proceedings of the 32 nd International Conference on Machine Learning, 2015. 
[30]
S. Adam, B. Sergey, B. Matthew, W. Daan,  and L. Timothy, "Meta-learning with memory-augmented neural networks", In Proceedings of The 33rd International Conference on Machine Learning, 2016, pp. 1842-1850. 
[31]
T. Chen, S. Kornblith, K. Swersky, M. Norouzi,  and G.E. Hinton, "Big self-supervised models are strong semi-supervised learners", Adv. Neural Inf. Process. Syst., vol. 33, pp. 22243-22255, 2020.
[32]
A. Halevy, P. Norvig,  and F. Pereira, "The unreasonable effectiveness of data", IEEE Intell. Syst., vol. 24, no. 2, pp. 8-12, 2009.
 [http://dx.doi.org/10.1109/MIS.2009.36]
[33]
J.L. Leevy, T.M. Khoshgoftaar, R.A. Bauder,  and N. Seliya, "A survey on addressing high-class imbalance in big data", J. Big Data, vol. 5, no. 1, p. 42, 2018.
 [http://dx.doi.org/10.1186/s40537-018-0151-6]
[34]
Y. Lecun, L. Bottou, Y. Bengio,  and P. Haffner, "Gradient-based learning applied to document recognition", Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
 [http://dx.doi.org/10.1109/5.726791]
[35]
I. Goodfellow, "Generative adversarial nets", Adv. Neural. Inf. Process Syst., vol. 27, 2014.
[36]
L.A. Gatys, A.S. Ecker,  and M. Bethge, "A neural algorithm of artistic style", arxiv, vol. 2015, p. 06576, 2015.
[37]
T. Karras, T. Aila, S. Laine,  and J. Lehtinen, "Progressive growing of gans for improved quality, stability, and variation", arxiv, vol. 2017, p. 10196, 2017.
[38]
Z. Barret,  and V. Le Quoc, "Neural architecture search with reinforcement learning", In Int. Conf. Learn. Represent., 2017. 
[39]
E.D. Cubuk, B. Zoph, D. Mane, V. Vasudevan,  and Q.V. Le, "Autoaugment: Learning augmentation policies from data", arxiv, vol. 2018, p. 09501, 2018.
[40]
J. Lemley, S. Bazrafkan,  and P. Corcoran, "Smart augmentation learning an optimal data augmentation strategy", IEEE Access, vol. 5, pp. 5858-5869, 2017.
 [http://dx.doi.org/10.1109/ACCESS.2017.2696121]
[41]
J. Tang, M. Sharma,  and R. Zhang, Explaining the Effect of Data Augmentation on Image Classification Tasks. Stanford University, 2020.
[42]
J.M. Wolterink, T. Leiner, M.A. Viergever,  and I. Išgum, "Generative adversarial networks for noise reduction in low-dose CT", IEEE Trans. Med. Imaging, vol. 36, no. 12, pp. 2536-2545, 2017.
 [http://dx.doi.org/10.1109/TMI.2017.2708987] [PMID:  28574346]
[43]
Y. Wang, B. Yu, L. Wang, C. Zu, D.S. Lalush, W. Lin, X. Wu, J. Zhou, D. Shen,  and L. Zhou, "3D conditional generative adversarial networks for high-quality PET image estimation at low dose", Neuroimage, vol. 174, pp. 550-562, 2018.
 [http://dx.doi.org/10.1016/j.neuroimage.2018.03.045] [PMID:  29571715]
[44]
D. Mahapatra,  and B. Bozorgtabar, "Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution", arxiv, vol. 2017, p. 04783, 2017.
[45]
F. Calimeri, A. Marzullo, C. Stamile,  and G. Terracina, "Biomedical data augmentation using generative adversarial neural networks", In:  International conference on artificial neural networks., Springer, 2017, pp. 626-634.
 [http://dx.doi.org/10.1007/978-3-319-68612-7_71]
[46]
C. Bermudez, A.J. Plassard, L.T. Davis, A.T. Newton, S.M. Resnick,  and B.A. Landman, "Learning implicit brain MRI manifolds with deep learning", SPIE, pp. 408-414, 2018.
[47]
M.J.M. Chuquicusma, S. Hussein, J. Burt,  and U. Bagci, "How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis", In IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 04-07 April, 2018. 
 [http://dx.doi.org/10.1109/ISBI.2018.8363564]
[48]
C. Baur, S. Albarqouni,  and N. Navab, "MelanoGANs: High resolution skin lesion synthesis with GANs", arxiv, vol. 2018, p. 04338, 2018.
[49]
A. Madani, M. Moradi, A. Karargyris,  and T. Syeda-Mahmood, "Chest x-ray generation and data augmentation for cardiovascular abnormality classification", SPIE, pp. 415-420, 2018.
[50]
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger,  and H. Greenspan, "Gan-based data augmentation for improved liver lesion classification", 
[51]
M.B. Ullah, "CPU based YOLO: A real time object detection algorithm", In IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 05-07 June, 2020. 
 [http://dx.doi.org/10.1109/TENSYMP50017.2020.9230778]
[52]
R. Girshick, J. Donahue, T. Darrell,  and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation", In Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, 23-28 June, 2014, pp. 580-587. 
 [http://dx.doi.org/10.1109/CVPR.2014.81]
[53]
J. Redmon, S. Divvala, R. Girshick,  and A. Farhadi, "You only look once: Unified, real-time object detection", In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27-30 June, 2016, pp. 779-788. 
 [http://dx.doi.org/10.1109/CVPR.2016.91]
[54]
O. Ronneberger, P. Fischer,  and T. Brox, "U-net: Convolutional networks for biomedical image segmentation", In 18th International Conference, Munich, Germany, 5-9 Oct, 2015, pp. 234-241. 
 [http://dx.doi.org/10.1007/978-3-319-24574-4_28]
[55]
V.D. Jadhav,  and L.V. Patil, "A Study on Medical Image Data Augmentation Using Learning Techniques", In:  ICT Analysis and Applications: Proceedings of ICT4SD 2022., Springer, 2022, pp. 23-30.
[56]
D. Liang, F. Yang, T. Zhang,  and P. Yang, "Understanding mixup training methods", IEEE Access, vol. 6, pp. 58774-58783, 2018.
 [http://dx.doi.org/10.1109/ACCESS.2018.2872698]
[57]
O. Parkhi, A. Vedaldi,  and A. Zisserman, "Deep face recognition", In British Machine Vision Conference, 2015. 
 [http://dx.doi.org/10.5244/C.29.41]
[58]
L. Li, Y. Peng, G. Qiu, Z. Sun,  and S. Liu, "A survey of virtual sample generation technology for face recognition", Artif. Intell. Rev., vol. 50, no. 1, pp. 1-20, 2018.
 [http://dx.doi.org/10.1007/s10462-016-9537-z]
[59]
L. Engstrom, B. Tran, D. Tsipras, L. Schmidt,  and A. Madry, "A rotation and a translation suffice: Fooling cnns with simple transformations",  2019. Available from: https://openreview.net/forum?id=BJfvknCqFQ
[60]
I.J. Goodfellow, J. Shlens,  and C. Szegedy, "Explaining and harnessing adversarial examples", arxiv, vol. 2014, p. 6572, 2014.
[61]
S. Li, Y. Chen, Y. Peng,  and L. Bai, "Learning more robust features with adversarial training", arxiv, vol. 2018, p. 07757, 2018.
[62]
L. Xie, J. Wang, Z. Wei, M. Wang,  and Q. Tian, "Disturblabel: Regularizing cnn on the loss layer", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June, 2016, pp. 4753-4762. 
 [http://dx.doi.org/10.1109/CVPR.2016.514]
[63]
C. Bowles, "Gan augmentation: Augmenting training data using generative adversarial networks", arxiv, vol. 2018, p. 10863, 2018.
[64]
C. Doersch, "Tutorial on variational autoencoders", arxiv, vol. 2016, p. 05908, 2016.
[65]
V.D.M. Laurens,  and G. Hinton, "Visualizing data using t-SNE", J. Mach. Learn. Res., vol. 9, no. 2605, pp. 2579-2605, 2008.
[66]
I. Masi, A.T. Trần, T. Hassner, J.T. Leksut,  and G. Medioni, "Do we really need to collect millions of faces for effective face recognition?", In 14th European Conference, Amsterdam, The Netherlands, 11-14 Oct, 2016, pp. 579-596. 
 [http://dx.doi.org/10.1007/978-3-319-46454-1_35]
[67]
I.J. Goodfellow, "Challenges in representation learning: A report on three machine learning contests", In 20th International Conference, ICONIP 2013, Daegu, Korea, 3-7 Nov, 2013, pp. 117-124. 
 [http://dx.doi.org/10.1007/978-3-642-42051-1_16]
[68]
L. Taylor,  and G. Nitschke, "Improving deep learning with generic data augmentation", In IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 18-21 Nov, 2018, pp. 1542-1547. 
 [http://dx.doi.org/10.1109/SSCI.2018.8628742]
[69]
J. Wang,  and L. Perez, "The effectiveness of data augmentation in image classification using deep learning", Convolut. Neural. Net. Vis. Recognit., vol. 11, pp. 1-8, 2017.
[70]
D. Ulyanov, A. Vedaldi,  and V. Lempitsky, "Instance normalization: The missing ingredient for fast stylization", arxiv, vol. 2016, p. 08022, 2016.
[71]
C.M. Shorten, "An exploration into synthetic data and generative adversarial", Transfer, vol. 4, p. 1, 2014.
[72]
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba,  and P. Abbeel, "Domain randomization for transferring deep neural networks from simulation to the real world", In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24-28 Sep, 2017, pp. 23-30. 
[73]
S.R. Richter, V. Vineet, S. Roth,  and V. Koltun, "Playing for data: Ground truth from computer games", In 14th European Conference, Amsterdam, The Netherlands, 11-14 Oct, 2016, pp. 102-118. 
 [http://dx.doi.org/10.1007/978-3-319-46475-6_7]
[74]
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang,  and R. Webb, "Learning from simulated and unsupervised images through adversarial training", In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 21-26 July, 2017, pp. 2107-2116. 
 [http://dx.doi.org/10.1109/CVPR.2017.241]
[75]
M. Cordts, "The cityscapes dataset for semantic urban scene understanding", In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27-30 June, 2016, pp. 3213-3223. 
 [http://dx.doi.org/10.1109/CVPR.2016.350]
[76]
E. Real, "Large-scale evolution of image classifiers", In International conference on machine learning, 2017, pp. 2902-2911. 
[77]
E. Real, A. Aggarwal, Y. Huang,  and Q.V. Le, "Regularized evolution for image classifier architecture search", In Proceedings of the aaai conference on artificial intelligence, 2019, pp. 4780-4789. 
 [http://dx.doi.org/10.1609/aaai.v33i01.33014780]
[78]
T. Salimans, J. Ho, X. Chen, S. Sidor,  and I. Sutskever, "Evolution strategies as a scalable alternative to reinforcement learning", arxiv, vol. 2017, p. 03864, 2017.
[79]
H. Mania, A. Guy,  and B. Recht, "Simple random search provides a competitive approach to reinforcement learning", arxiv, vol. 2018, p. 07055, 2018.
[80]
N. Dalal,  and B. Triggs, "Histograms of oriented gradients for human detection", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 20-25 June, 2005. 
 [http://dx.doi.org/10.1109/CVPR.2005.177]
[81]
D.G. Lowe, "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
 [http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94]
[82]
R.S. Sutton,  and A.G. Barto, "The reinforcement learning problem", In:  Reinforcement learning: An introduction., MIT Press Cambridge: MA, 1998, pp. 51-85.
[83]
M. Geng, K. Xu, B. Ding, H. Wang,  and L. Zhang, "Learning data augmentation policies using augmented random search", arxiv, vol. 2018, p. 04768, 2018.
[84]
T.N. Minh, M. Sinn, H.T. Lam,  and M. Wistuba, "Automated image data preprocessing with deep reinforcement learning", arxiv, vol. 2018, p. 05886, 2018.
[85]
S. Hochreiter, "The vanishing gradient problem during learning recurrent neural nets and problem solutions", Int. J. Uncertain. Fuzziness Knowl. Based Syst., vol. 6, no. 2, pp. 107-116, 1998.
 [http://dx.doi.org/10.1142/S0218488598000094]
[86]
I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari,  and K. He, "Data distillation: Towards omni-supervised learning", In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4119-4128. 
[87]
G. Wang, W. Li, M. Aertsen, J. Deprest, S. Ourselin,  and T. Vercauteren, "Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation", 
[88]
K. Matsunaga, A. Hamada, A. Minagawa,  and H. Koga, "Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble", arxiv, vol. 2017, p. 03108, 2017.
[89]
Y. Bengio, J. Louradour, R. Collobert,  and J. Weston, "Curriculum learning", In Proceedings of the 26th annual international conference on machine learning, Montreal, Quebec, Canada, 14-18 June, 2009, pp. 41-48. 
 [http://dx.doi.org/10.1145/1553374.1553380]
[90]
R. Wu, S. Yan, Y. Shan, Q. Dang,  and G. Sun, "Deep image: Scaling up image recognition", arxiv, vol. 2015, p. 02876, 2015.
[91]
C. Dong, C.C. Loy, K. He,  and X. Tang, "Learning a deep convolutional network for image super-resolution", In 13th European Conference, Zurich, Switzerland, 6-12 Oct, 2014, pp. 184-199. 
 [http://dx.doi.org/10.1007/978-3-319-10593-2_13]
[92]
C. Ledig, "Photo-realistic single image super-resolution using a generative adversarial network", In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 21-26 July, 2017, pp. 4681-4690. 
 [http://dx.doi.org/10.1109/CVPR.2017.19]
[93]
H. Zhang, "Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks", In Proceedings of the IEEE international conference on computer vision, Venice, Italy, 22-29 Oct, 2017, pp. 5907-5915. 
 [http://dx.doi.org/10.1109/ICCV.2017.629]
[94]
A. Radford, L. Metz,  and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks", arxiv, vol. 2015, p. 06434, 2015.
[95]
L. Tran,  and X. Liu, "Nonlinear 3d face morphable model", In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7346-7355. 
[96]
M. Buda, A. Maki,  and M.A. Mazurowski, "A systematic study of the class imbalance problem in convolutional neural networks", Neural Netw., vol. 106, pp. 249-259, 2018.
 [http://dx.doi.org/10.1016/j.neunet.2018.07.011] [PMID:  30092410]
[97]
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs,  and H. Lipson, "Understanding neural networks through deep visualization", arxiv, vol. 2015, p. 06579, 2015.
[98]
T. Kim, M. Cha, H. Kim, J.K. Lee,  and J. Kim, "Learning to discover cross-domain relations with generative adversarial networks", In International conference on machine learning, 2017, pp. 1857-1865. 
[99]
X. Mao, Q. Li, H. Xie, R.Y.K. Lau, Z. Wang,  and S. Paul Smolley, "Least squares generative adversarial networks", In Proceedings of the IEEE international conference on computer vision, 2017, pp. 2794-2802. 
[100]
J.J. Lv, X.H. Shao, J.S. Huang, X.D. Zhou,  and X. Zhou, "Data augmentation for face recognition", Neurocomputing, vol. 230, pp. 184-196, 2017.
 [http://dx.doi.org/10.1016/j.neucom.2016.12.025]
[101]
Y. Choi, M. Choi, M. Kim, J-W. Ha, S. Kim,  and J. Choo, "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation", In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 18-23 June, 2018, pp. 8789-8797. 
 [http://dx.doi.org/10.1109/CVPR.2018.00916]
[102]
Z. He, W. Zuo, M. Kan, S. Shan,  and X. Chen, "Attgan: Facial attribute editing by only changing what you want", IEEE Trans. Image Process., vol. 28, no. 11, pp. 5464-5478, 2019.
 [http://dx.doi.org/10.1109/TIP.2019.2916751] [PMID:  31107649]
[103]
J. Guo, X. Zhu, Z. Lei,  and S.Z. Li, "Face synthesis for eyeglass-robust face recognition", In 13th Chinese Conference, CCBR 2018, Urumqi, China, 11-12 Aug, 2018, pp. 275-284. 
[104]
X. Zhang, Z. Wang, D. Liu,  and Q. Ling, "Dada: Deep adversarial data augmentation for extremely low data regime classification", In Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp), Brighton, UK, 12-17 May, 2019, pp. 2807-2811. 
 [http://dx.doi.org/10.1109/ICASSP.2019.8683197]
[105]
M. Abadi, "TensorFlow: a system for {Large-Scale} machine learning", In 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016, pp. 265-283. 
[106]
A. Gulli,  and S. Pal, Deep learning with Keras. Packt Publishing Ltd, 2017.
[107]
A. Rozo, J. Moeyersons, J. Morales, R. Garcia van der Westen, L. Lijnen, C. Smeets, S. Jantzen, V. Monpellier, D. Ruttens, C. Van Hoof, S. Van Huffel, W. Groenendaal,  and C. Varon, "Data augmentation and transfer learning for data quality assessment in respiratory monitoring", Front. Bioeng. Biotechnol., vol. 10, p. 806761, 2022.
 [http://dx.doi.org/10.3389/fbioe.2022.806761] [PMID:  35237576]
[108]
M. Bornea, L. Pan, S. Rosenthal, R. Florian,  and A. Sil, "Multilingual transfer learning for QA using translation as data augmentation", In Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 12583-12591. 
 [http://dx.doi.org/10.1609/aaai.v35i14.17491]
[109]
M. Loey, G. Manogaran,  and N.E.M. Khalifa, "A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images", Neural Comput. Appl., pp. 1-13, 2020.
 [http://dx.doi.org/10.1007/s00521-020-05437-x] [PMID:  33132536]
[110]
M.M. Islam, M.B. Hossain, M.N. Akhtar, M.A. Moni,  and K.F. Hasan, "CNN based on transfer learning models using data augmentation and transformation for detection of concrete crack", Algorithms, vol. 15, no. 8, p. 287, 2022.
 [http://dx.doi.org/10.3390/a15080287]
[111]
X. Jiang, T. Gao, Z. Zhu,  and Y. Zhao, "Real-time face mask detection method based on YOLOv3", Electronics, vol. 10, no. 7, p. 837, 2021.
 [http://dx.doi.org/10.3390/electronics10070837]
[112]
M.A. Wakili, "Classification of breast cancer histopathological images using densenet and transfer learning", Comput. Intell. Neurosci., vol. 2022, 2022.
[113]
T. Boot,  and H. Irshad, "Diagnostic assessment of deep learning algorithms for detection and segmentation of lesion in mammographic images", In 23rd International Conference, Lima, Peru, 4-8 Oct, 2020, pp. 55-65. 
 [http://dx.doi.org/10.1007/978-3-030-59719-1_6]
[114]
T. Arshad, Z. Junping,  and Q. Wang, "Multiclass classification of remote sensing images using deep learning techniques", In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 16-21 July, 2023. 
 [http://dx.doi.org/10.1109/IGARSS52108.2023.10283456]
[115]
G. Ayana, J. Park, J.W. Jeong,  and S. Choe, "A novel multistage transfer learning for ultrasound breast cancer image classification", Diagnostics, vol. 12, no. 1, p. 135, 2022.
 [http://dx.doi.org/10.3390/diagnostics12010135] [PMID:  35054303]
[116]
E. Li, L. Wang, Q. Xie, R. Gao, Z. Su,  and Y. Li, "A novel deep learning method for maize disease identification based on small sample-size and complex background datasets", Ecol. Inform., vol. 75, p. 102011, 2023.
 [http://dx.doi.org/10.1016/j.ecoinf.2023.102011]
[117]
F. Yousaf, S. Iqbal, N. Fatima, T. Kousar,  and M. Shafry Mohd Rahim, "Multi-class disease detection using deep learning and human brain medical imaging", Biomed. Signal Process. Control, vol. 85, p. 104875, 2023.
 [http://dx.doi.org/10.1016/j.bspc.2023.104875]
[118]
A.L.C. Ottoni, R.M. de Amorim, M.S. Novo,  and D.B. Costa, "Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets", Int. J. Mach. Learn. Cybern., vol. 14, no. 1, pp. 171-186, 2023.
 [http://dx.doi.org/10.1007/s13042-022-01555-1] [PMID:  35432624]
[119]
L. Alzubaidi, J. Bai, A. Al-Sabaawi, J. Santamaría, A.S. Albahri, B.S.N. Al-dabbagh, M.A. Fadhel, M. Manoufali, J. Zhang, A.H. Al-Timemy, Y. Duan, A. Abdullah, L. Farhan, Y. Lu, A. Gupta, F. Albu, A. Abbosh,  and Y. Gu, "A survey on deep learning tools dealing with data scarcity: Definitions, challenges, solutions, tips, and applications", J. Big Data, vol. 10, no. 1, p. 46, 2023.
 [http://dx.doi.org/10.1186/s40537-023-00727-2]
[120]
P. Hager, M.J. Menten,  and D. Rueckert, "Best of both worlds: Multimodal contrastive learning with tabular and imaging data", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17-24 June, 2023, pp. 23924-23935. 
 [http://dx.doi.org/10.1109/CVPR52729.2023.02291]
[121]
V.H. Phung,  and E.J. Rhee, "A deep learning approach for classification of cloud image patches on small datasets", J. Inform. Commun. Converg. Eng., vol. 16, no. 3, p. 173, 2018. (178).
[122]
H. Inoue, "Data augmentation by pairing samples for images classification", arxiv, vol. 2018, p. 02929, 2018.
[123]
Y. Zhu, "CPSNet: A cyclic pyramid-based small lesion detection network", Multimed Tools Appl., pp. 1-19, 2023.
[124]
H. Li, J. Li, X. Guan, B. Liang, Y. Lai,  and X. Luo, "Research on overfitting of deep learning", In 15th International Conference on Computational Intelligence and Security (CIS), Macao, China, 13-16 Dec, 2019. 
 [http://dx.doi.org/10.1109/CIS.2019.00025]
[125]
C.C. Aggarwal, "Rare class learning", In:  Data Classification: Algorithms and Applications., CRC Press, 2014.
[126]
O. Day,  and T.M. Khoshgoftaar, "A survey on heterogeneous transfer learning", J. Big Data, vol. 4, no. 1, p. 29, 2017.
 [http://dx.doi.org/10.1186/s40537-017-0089-0]
[127]
T. Hospedales, A. Antoniou, P. Micaelli,  and A. Storkey, "Meta-learning in neural networks: A survey", IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 5149-5169, 2022.
 [PMID:  33974543]
[128]
Z. Wang, X. Tang, W. Luo,  and S. Gao, "Face aging with identity-preserved conditional generative adversarial networks", In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7939-7947. 
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DOI https://dx.doi.org/10.2174/0126662558286875231215054324	Print ISSN 2666-2558
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