In recent years, with the development of science and technology, powerful computing devices have been constantly developing. As an important foundation, deep learning (DL) technology has achieved many successes in multiple fields. In addition, the success of deep learning also relies on the support of large-scale datasets, which can provide models with a variety of images. The rich information in these images can help the model learn more about various categories of images, thereby improving the classification performance and generalization ability of the model. However, in real application scenarios, it may be difficult for most tasks to collect a large number of images or enough images for model training, which also restricts the performance of the trained model to a certain extent. Therefore, how to use limited samples to train the model with high performance becomes key. In order to improve this problem, the few-shot learning (FSL) strategy is proposed, which aims to obtain a model with strong performance through a small amount of data. Therefore, FSL can play its advantages in some real scene tasks where a large number of training data cannot be obtained. In this review, we will mainly introduce the FSL methods for image classification based on DL, which are mainly divided into four categories: methods based on data enhancement, metric learning, meta-learning and adding other tasks. First, we introduce some classic and advanced FSL methods in the order of categories. Second, we introduce some datasets that are often used to test the performance of FSL methods and the performance of some classical and advanced FSL methods on two common datasets. Finally, we discuss the current challenges and future prospects in this field.
Citation: Wu Zeng, Zheng-ying Xiao. Few-shot learning based on deep learning: A survey[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 679-711. doi: 10.3934/mbe.2024029
In recent years, with the development of science and technology, powerful computing devices have been constantly developing. As an important foundation, deep learning (DL) technology has achieved many successes in multiple fields. In addition, the success of deep learning also relies on the support of large-scale datasets, which can provide models with a variety of images. The rich information in these images can help the model learn more about various categories of images, thereby improving the classification performance and generalization ability of the model. However, in real application scenarios, it may be difficult for most tasks to collect a large number of images or enough images for model training, which also restricts the performance of the trained model to a certain extent. Therefore, how to use limited samples to train the model with high performance becomes key. In order to improve this problem, the few-shot learning (FSL) strategy is proposed, which aims to obtain a model with strong performance through a small amount of data. Therefore, FSL can play its advantages in some real scene tasks where a large number of training data cannot be obtained. In this review, we will mainly introduce the FSL methods for image classification based on DL, which are mainly divided into four categories: methods based on data enhancement, metric learning, meta-learning and adding other tasks. First, we introduce some classic and advanced FSL methods in the order of categories. Second, we introduce some datasets that are often used to test the performance of FSL methods and the performance of some classical and advanced FSL methods on two common datasets. Finally, we discuss the current challenges and future prospects in this field.
| [1] |
H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, T. Ganslandt, Transfer learning for medical image classification: A literature review, BMC Med. Imaging, 22 (2022), 69. https://doi.org/10.1186/s12880-022-00793-7 doi: 10.1186/s12880-022-00793-7
|
| [2] |
Z. X. Zou, K. Y. Chen, Z. W. Shi, Y. H. Guo, J. P. Ye, Object detection in 20 years: A survey, Proc. IEEE, 111 (2023), 257–276. https://doi.org/10.1109/JPROC.2023.3238524 doi: 10.1109/JPROC.2023.3238524
|
| [3] | H. Q. Zhao, W. B. Zhou, D. D. Chen, T. Y. Wei, N. H. Yu, Multi-attentional deepfake detection, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE 8 (2021), 2185–2194. https://doi.org/10.1109/CVPR46437.2021.00222 |
| [4] | I. Goodfellow, P. A. Jean, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial nets, in Advances in Neural Information Processing Systems, 27 (2014), 1–9. |
| [5] |
B. Pandey, D. K. Pandey, B. P. Mishra, W. Rhmann, A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions, J. King Saud Univ. Comput. Inf. Sci., 34 (2022), 5083–5099. https://doi.org/10.1016/j.jksuci.2021.01.007 doi: 10.1016/j.jksuci.2021.01.007
|
| [6] | P. Li, X. H. Xu, Recurrent compressed convolutional networks for short video event detection, in IEEE Access, 8 (2020), 114162–114171. https://doi.org/10.1109/ACCESS.2020.3003939 |
| [7] |
P. Li, Q. H. Ye, L. M. Zhang, L.Yuan, X. H. Xu, L. Shao, Exploring global diverse attention via pairwise temporal relation for video summarization, Pattern Recogn., 111 (2021), 107677. https://doi.org/10.1016/j.patcog.2020.107677 doi: 10.1016/j.patcog.2020.107677
|
| [8] |
P. Li, P. Zhang, T. Wang, H. X. Xiao, Time–frequency recurrent transformer with diversity constraint for dense video captioning, Inform. Process. Manag., 60 (2023), 103204. https://doi.org/10.1016/j.ipm.2022.103204 doi: 10.1016/j.ipm.2022.103204
|
| [9] |
P. Li, J. C. Cao, L. Yuan, Q. H. Ye, X. H. Xu, Truncated attention-aware proposal networks with multi-scale dilation for temporal action detection, Pattern Recogn., 142 (2023), 109684. https://doi.org/10.1016/j.patcog.2023.109684 doi: 10.1016/j.patcog.2023.109684
|
| [10] |
P. Li, Y. Zhang a, L. Yuan, H. X. Xiao, B. B. Lin, X. H. Xu, Efficient long-short temporal attention network for unsupervised video object segmentation, Pattern Recogn., 146 (2024), 110078. https://doi.org/10.1016/j.patcog.2023.110078 doi: 10.1016/j.patcog.2023.110078
|
| [11] |
K. Feng, J. C. Ji, Y. C. Zhang, Q. Ni, Z. Liu, M. Beer, Digital twin-driven intelligent assessment of gear surface degradation, Mechan. Syst. Signal Process., 186 (2023), 109896. https://doi.org/10.1016/j.ymssp.2022.109896 doi: 10.1016/j.ymssp.2022.109896
|
| [12] |
Y. D. Xu, K. Feng, X. A. Yan, R. Q. Yan, Q. Ni, B. B. Sun, et al., CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery, Inform. Fusion, 95 (2023), 1–16. https://doi.org/10.1016/j.inffus.2023.02.012 doi: 10.1016/j.inffus.2023.02.012
|
| [13] | K. Feng, Y. D. Xu, Y. L. Wang, S. Li, Q. B. Jiang, B. B. Sun, et al., Digital twin enabled domain adversarial graph networks for bearing fault diagnosis, in IEEE Transactions on Industrial Cyber-Physical Systems, 1 (2023), 113–122. https://doi.org/10.1109/TICPS.2023.3298879 |
| [14] |
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., ImageNet large scale visual recognition challenge, Int J Comput Vis, 115 (2015), 211–252. https://doi.org/10.1007/s11263-015-0816-y doi: 10.1007/s11263-015-0816-y
|
| [15] | K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2016), 770–778. https://doi.org/10.1109/CVPR.2016.90 |
| [16] | A. G. Howard, M. L. Zhu, B. Chen, D. Kalenichenko, W. J. Wang, T. Weyand, et al., MobileNets: Efficient convolutional neural networks for mobile vision applications, preprint, arXiv: 1704.04861. |
| [17] | X. Y. Zhang, X. Y. Zhou, M. X. Lin, J. Sun, ShuffleNet: An extremely efficient convolutional neural network for mobile devices, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 6848–6856. https://doi.org/10.1109/CVPR.2018.00716 |
| [18] | G. Huan, Z. Liu, L. V. D. Maaten, K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2017), 2261–2269. https://doi.org/10.1109/CVPR.2017.243 |
| [19] | W. H. Yu, M. Luo, P. Zhou, C. Y. Si, Y. C. Zhou, X. C. Wang, et al., MetaFormer is actually what you need for vision, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 10809–10819. https://doi.org/10.1109/CVPR52688.2022.01055 |
| [20] | Y. P. Chen, X. Y. Dai, D. D. Chen, M. C. Liu, X. Dong, L. Yuan, et al., Mobile-former: Bridging mobilenet and transforme, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 5270–5279. https://doi.org/10.1109/CVPR52688.2022.00520 |
| [21] |
Y. T. Vuong, Q. M. Bui, H. Nguyen, T. Nguyen, V. Tran, X. Phan, et al., SM-BERT-CR: A deep learning approach for case law retrieval with supporting model, Artif. Intell. Law, 31 (2023), 601–628. https://doi.org/10.1007/s10506-022-09319-6 doi: 10.1007/s10506-022-09319-6
|
| [22] | J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, F. F. Li, ImageNet: A large-scale hierarchical image database, in 2009 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2009), 248–255. https://doi.org/10.1109/CVPR.2009.5206848 |
| [23] | T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, et al., Microsoft COCO: Common objects in context, in 2014 European conference computer vision (ECCV), (2014), 740–755. https://doi.org/10.1007/978-3-319-10602-1_48 |
| [24] |
J. C. Yang, X. L. Guo, Y. Li, F. Marinello, S. Ercisli, Z. Zhang, A survey of few-shot learning in smart agriculture: developments, applications and challenges, Plant Methods., 18 (2022), 28. https://doi.org/10.1186/s13007-022-00866-2 doi: 10.1186/s13007-022-00866-2
|
| [25] |
J. D. Chen, J. X. Chen, D.F. Zhang, Y. D. Sun, Y. A. Nanehkaran, Using deep transfer learning for image-based plant disease identification, Comput. Electron. Agri., 173 (2020), 105393. https://doi.org/10.1016/j.compag.2020.105393 doi: 10.1016/j.compag.2020.105393
|
| [26] |
S. Q. Jiang, W. Q. Min, Y. Q. Lyu, L. H. Liu, Few-shot food recognition via multi-view representation learning, ACM Transact. Multi. Comput. Commun. Appl., 16 (2020), 1–20. https://doi.org/10.1145/3391624 doi: 10.1145/3391624
|
| [27] |
J. Yang, X. M. Wang, Z. P. Luo, Few-shot remaining useful life prediction based on meta-learning with deep sparse kernel network, Inform. Sci., 653 (2024), 119795. https://doi.org/10.1016/j.ins.2023.119795 doi: 10.1016/j.ins.2023.119795
|
| [28] |
Y. Q. Wang, Q. M. Yao, J. T. Kwok, L. M. Ni, Generalizing from a few examples: A survey on few-shot learning, ACM Comput. Surveys, 53 (2020), 1–34. https://doi.org/10.1145/3386252 doi: 10.1145/3386252
|
| [29] | J. Lu, P. H. Gong, J. P. Ye, C. H. Zhang, Learning from very few samples: A survey, preprint, arXiv: 2009.02653. |
| [30] |
X. X. Li, X. C. Yang, Z. Y. Ma, J. H. Xue, Deep metric learning for few-shot image classification: A Review of recent developments, Pattern Recogn., 138 (2023), 109381. https://doi.org/10.1016/j.patcog.2023.109381 doi: 10.1016/j.patcog.2023.109381
|
| [31] | A. Dabouei, S. Soleymani, F. Taherkhani, N. M. Nasrabadi, SuperMix: Supervising the mixing data augmentation, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 13789–13798. https://doi.org/10.1109/CVPR46437.2021.01358 |
| [32] | M. Hong, J. Choi, G. Kim, StyleMix: Separating content and style for enhanced data augmentation, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 14857–14865. https://doi.org/10.1109/CVPR46437.2021.01462 |
| [33] |
N. E. Khalifa, M. Loey, S. Mirjalili, A comprehensive survey of recent trends in deep learning for digital images augmentation, Artif. Intell. Rev., 55 (2022), 2351–2377. https://doi.org/10.1007/s10462-021-10066-4 doi: 10.1007/s10462-021-10066-4
|
| [34] | E. D. Ubuk, B. Zoph, D. Mané, V. Vasudevan, Q. V. Le, AutoAugment: learning augmentation strategies from data, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 113–123. https://doi.org/10.1109/CVPR.2019.00020 |
| [35] | T. DeVries, G. W. Taylor, Improved regularization of convolutional neural networks with cutout, preprint, arXiv: 1708.04552. |
| [36] | J. Y. Zhu, T. Park, P. Isola, A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, (2017), 2242–2251. https://doi.org/10.1109/ICCV.2017.244 |
| [37] | T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive growing of GANs for improved quality, stability and variation, preprint, arXiv: 1710.10196. |
| [38] | Z. T. Chen, Y. W. Fu, Y. X. Wang, L. Ma, W. Liu, M. Hebert, Image deformation meta-networks for one-Shot learning, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 8672–8681. https://doi.org/10.1109/CVPR.2019.00888 |
| [39] | S. Yun, D. Han, S. Chun, S. J. Oh, S. Chun, J. Choe, Y. Yoo, CutMix: Regularization strategy to train strong classifiers with localizable features, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, (2019), 6022–6031. https://doi.org/10.1109/ICCV.2019.00612 |
| [40] | S. Khodadadeh, L. Boloni, M. Shah, Unsupervised meta-learning for few-shot image classification, in 2019 Advances in Neural Information Processing Systems (NIPS), (2019). |
| [41] | A. Antoniou, A. Storkey, Assume, augment and learn: Unsupervised few-shot meta-learning via random labels and data augmentation, preprint, arXiv: 1902.09884. |
| [42] | T. X. Qin, W. B. Li, Y. H. Shi, Y. Gao, Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation, preprint, arXiv: 2004.05805. |
| [43] |
H. Xu, J. X. Wang, H. Li, D. Q. Ouyang, J. Shao, Unsupervised meta-learning for few-shot learning, Pattern Recogn., 116 (2021), 107951. https://doi.org/10.1016/j.patcog.2021.107951 doi: 10.1016/j.patcog.2021.107951
|
| [44] | M. Tao, H. Tang, F. Wu, X. Y. Jing, B. K. Bao, C. S. Xu, DF-GAN: A simple and effective baseline for text-to-image synthesis, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 16494–16504. https://doi.org/10.1109/CVPR52688.2022.01602 |
| [45] | W. T. Liao, K. Hu, M. Y. Yang, B. Rosenhahn, Text to image generation with semantic-spatial aware GAN, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 18166–18175. https://doi.org/10.1109/CVPR52688.2022.01765 |
| [46] | X. T. Wu, H. B. Zhao, L. L. Zheng, S. H. Ding, X. Li, Adma-GAN: Attribute-driven memory augmented GANs for text-to-image generation, in Proceedings of the 30th ACM International Conference on Multimedia, ACM, (2022), 1593–1602. https://doi.org/10.1145/3503161.3547821 |
| [47] | A. Mehrotra, A. Dukkipati, Generative adversarial residual pairwise networks for one shot learning, preprint, arXiv: 1703.08033. |
| [48] | Y. X. Wang, R. Girshick, M. Hebert, B. Hariharan, Low-shot learning from imaginary data, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 7278–7286. https://doi.org/10.1109/CVPR.2018.00760 |
| [49] | R. X. Zhang, T. Che, Z. Ghahramani, Y. Bengio, Y. Q. Song, MetaGAN: An adversarial approach to few-Shot learning, in 2018 Advances in Neural Information Processing Systems (NIPS), (2018). |
| [50] | E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, et al., Delta-encoder: an effective sample synthesis method for few-shot object recognition, in 2018 Advances in Neural Information Processing Systems (NIPS), (2018). |
| [51] | Y. Q. Xian, S. Sharma, B. Schiele, Z. Akata, F-VAEGAN-D2: A Feature generating framework for any-shot learning, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 10267–102765. https://doi.org/10.1109/CVPR.2019.01052 |
| [52] | K. Li, Y. L. Zhang, K. P. Li, Y. Fu, Adversarial feature hallucination networks for few-shot learning, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2020), 13467–13476. https://doi.org/10.1109/CVPR42600.2020.01348 |
| [53] | F. Pahde, P. Jähnichen, T. Klein, M. Nabi, Cross-modal hallucination for few-shot fine-grained recognition, preprint, arXiv: 1806.05147. |
| [54] | M. Dixit, R. Kwitt, M. Niethammer, N. Vasconcelos, AGA: Attribute-guided augmentation, in 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2017), 3328–3336. https://doi.org/10.1109/CVPR.2017.355 |
| [55] | B. Liu, X. D. Wang, M. Dixit, R. Kwitt, N. Vasconcelos, Feature space transfer for data augmentation, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 9090–9098. https://doi.org/10.1109/CVPR.2018.00947 |
| [56] | Z. T. Chen, Y. W. Fu, Y. D. Zhang, Y. G. Jiang, X. Y. Xue, L. Sigal, Multi-level semantic feature augmentation in few-shot learning, preprint, arXiv: 1804.05298. |
| [57] | H. G. Zhang, J. Zhang, P. Koniusz, Few-shot learning via saliency-guided hallucination of samples, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 2765–2774. https://doi.org/10.1109/CVPR.2019.00288 |
| [58] | G. Koch, R. Zemel, R. Salakhutdinov, Siamese neural networks for one-shot image recognition, in 2015 International Conference on Machine Leaning (ICML), (2015). |
| [59] | O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, D. Wierstra, Matching networks for one shot learning, in 2019 Advances in Neural Information Processing Systems (NIPS), (2019). |
| [60] | J. Snell, K. Swersky, R. Zemel, Prototypical networks for few-shot learning, in 2017 Advances in Neural Information Processing Systems (NIPS), (2017). |
| [61] | F. Sung, Y. X. Yang, Li, Zhang, T. Xiang, P. H.S. Torr, T. M. Hospedales, Learning to compare: Relation network for few-shot learning, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 1199–1208. https://doi.org/10.1109/CVPR.2018.00131 |
| [62] | W. B. Li, L. Wang, J. L. Xu, J. Huo, Y. Gao, J. B. Luo, Revisiting local descriptor based image-to-class measure for few-shot learning, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 7253–7260. https://doi.org/10.1109/CVPR.2019.00743 |
| [63] | Y. B. Liu, J. H. Lee, M. Park, S. Kim, E. Yang, S. J. Hwang, et al., Learning to propagate labels: Transductive propagation network for few-shot learning, preprint, arXiv: 1805.10002. |
| [64] | C. Simon, P. Koniusz, R. Nock, M. Harandi, Adaptive Subspaces for Few-Shot Learning, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2020), 4135–4144. https://doi.org/10.1109/CVPR42600.2020.00419 |
| [65] | K. Allen, E. Shelhamer, H. Shin, J. Tenenbaum, Infinite mixture prototypes for few-shot learning, in 2019 International Conference on Machine Leaning (ICML), (2019), 232–241. |
| [66] | C. Xing, N. Rostamzadeh, B. Oreshkin, P. O. O. Pinheiro, Adaptive cross-modal few-shot learning, in 2019 Advances in Neural Information Processing Systems (NIPS), (2019). |
| [67] |
X. M. Li, L. Q. Yu, C. W. Fu, M. Fang, P.-A. Heng, Revisiting metric learning for few-shot image classification, Neurocomputing, 406 (2020), 49–58. https://doi.org/10.1016/j.neucom.2020.04.040 doi: 10.1016/j.neucom.2020.04.040
|
| [68] | S. P. Yan, S. Y. Zhang, X. M. He, A dual attention network with semantic embedding for few-shot learning, in 2019 Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), (2019), 9079–9086. https://doi.org/10.1609/aaai.v33i01.33019079 |
| [69] |
P. Li, G. P. Zhao, X. H. Xu, Coarse-to-fine few-shot classification with deep metric learning, Inform.n Sci., 610 (2022), 592–604. https://doi.org/10.1016/j.ins.2022.08.048 doi: 10.1016/j.ins.2022.08.048
|
| [70] | T. Y. Gao, X. Han, Z. Y. Liu, M. S. Sun, Hybrid attention-based prototypical networks for noisy few-shot relation classification, in 2019 Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), (2019), 6407–6414. https://doi.org/10.1609/aaai.v33i01.33016407 |
| [71] | B. Oreshkin, P. R. López, A. Lacoste, Tadam: Task dependent adaptive metric for improved few-shot learning, in 2018 Advances in Neural Information Processing Systems (NIPS), (2018) |
| [72] | H. Y. Li, D. Eigen, S. Dodge, M. Zeiler, X. G. Wang, Finding task-relevant features for few-shot learning by category traversal, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 1–10. https://doi.org/10.1109/CVPR.2019.00009 |
| [73] | F. Y. Yang, R. P. Wang, X. L. Chen, SEGA: Semantic guided attention on visual prototype for few-shot learning, in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, (2022), 1586–1596. https://doi.org/10.1109/WACV51458.2022.00165 |
| [74] | R. B. Hou, H. Chang, B. P. Ma, S. G. Shan, X. L. Chen, Cross attention network for few-shot classification, in 2019 Advances in Neural Information Processing Systems (NIPS), (2019). |
| [75] | A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, T. Lillicrap, One-shot with memory-augmented neural networks, preprint, arXiv: 1605.06065. |
| [76] | C. Finn, P. Abbeel, S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, in 2017 International Conference on Machine Leaning (ICML), (2017), 1126–1135. |
| [77] | A. Nichol, J. Achiam, J. Schulman, On first-order meta-learning algorithms, preprint, arXiv: 1803.02999. |
| [78] | A. Antoniou, H. Edwards, A. Storkey, How to train your MAML, preprint, arXiv: 1810.09502. |
| [79] | S. Ravi, H. Larochelle, Optimization as a model for few-shot learning, in 2017 International Conference on Learning Representations (ICLR), (2017) |
| [80] | S. Gidaris, N. Komodakis, Dynamic few-shot visual learning without forgetting, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2018), 4367–4375. https://doi.org/10.1109/CVPR.2018.00459 |
| [81] | Q. R. Sun, Y. Y. Liu, T. S. Chua, B. Schiele, Meta-transfer learning for few-shot learning, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 403–412. https://doi.org/10.1109/CVPR.2019.00049 |
| [82] | H. J. Ye, H. X. Hu, D. C. Zhan, F. Sha, Few-shot learning via embedding adaptation with set-to-set functions, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2020), 8805–8814. https://doi.org/10.1109/CVPR42600.2020.00883 |
| [83] | K. Lee, S. Maji, A. Ravichandran, S. Soatto, Meta-learning with differentiable convex optimization, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2019), 10649–10657. https://doi.org/10.1109/CVPR.2019.01091 |
| [84] | C. Zhang, H. H. Ding, G. S. Lin, R. B. Li, C. H. Wang, C. H. Shen, Meta navigator: Search for a Good Adaptation Policy for Few-shot Learning, in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, (2021), 9415–9424. https://doi.org/10.1109/ICCV48922.2021.00930 |
| [85] | A. Aimen, S. Sidheekh, N. C. Krishnan, Task attended meta-learning for few-shot learning, preprint, arXiv: 2106.10642. |
| [86] |
R. Krishnan, P. Rajpurkar, E. J. Topol, Self-supervised learning in medicine and healthcare, Nature Biomedical Engineering., 6 (2022), 1346–1352. https://doi.org/10.1038/s41551-022-00914-1 doi: 10.1038/s41551-022-00914-1
|
| [87] | S. Gidaris, P. Singh, N. Komodakis, Unsupervised representation learning by predicting image rotations, preprint, arXiv: 1803.07728. |
| [88] | W. X. Wang, J. Li, H. Ji, Self-supervised deep image restoration via adaptive stochastic gradient langevin dynamics, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 1979–1988. https://doi.org/10.1109/CVPR52688.2022.00203 |
| [89] | H. Q. Wang, X. Guo, Z. H. Deng, Y. Lu, Rethinking minimal sufficient representation in contrastive learning, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2022), 16020-16029. https://doi.org/10.1109/CVPR52688.2022.01557 |
| [90] | M. L. Zhang, J. H. Zhang, Z. W. Lu, T. Xiang, M. Y. Ding, S. F. Huang, IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning, in 2021 International Conference on Learning Representations (ICLR), (2021) |
| [91] | X. Luo, Y. X. Chen, L. J. Wen, L. L. Pan, Z. L. Xu, Boosting few-shot classification with view-learnable contrastive learning, in 2021 IEEE International Conference on Multimedia and Expo (ICME), IEEE, (2021), 1–6. https://doi.org/10.1109/ICME51207.2021.9428444 |
| [92] |
T. Lee, S. Yoo, Augmenting few-shot learning with supervised contrastive learning, IEEE Access., 9 (2021), 61466-61474. https://doi.org/10.1109/ACCESS.2021.3074525 doi: 10.1109/ACCESS.2021.3074525
|
| [93] | Z. Y. Yang, J. H. Wang, Y. Y. Zhu, Few-shot classification with contrastive learning, in 2022 European conference computer vision (ECCV), (2022), 293–309. https://doi.org/10.1007/978-3-031-20044-1_17 |
| [94] | Y. N. Lu, L. J. Wen, J. Z. Liu, Self-supervision can be a good few-shot learner, in 2022 European conference computer vision (ECCV), (2022), 740–758. https://doi.org/10.1007/978-3-031-19800-7_43 |
| [95] | S. Fort, Gaussian prototypical networks for few-shot learning on omniglot, preprint, arXiv: 1708.02735. |
| [96] | L. Bertinetto, J. F. Henriques, P. H.S. Torr, A. Vedaldi, Meta-learning with differentiable closed-form solvers, preprint, arXiv: 1805.08136. |
| [97] | C. Wah, S. Branson, P. Welinder, P. Perona, S. Belongie, The caltech-ucsd birds-200-2011 dataset: Technical report CNS-TR-2011-001, (2011), 1–8. |
| [98] | A. Khosla, N. Jayadevaprakash, B. P. Yao, F. F. Li, Novel dataset for fine-grained image categorization: stanford dogs, CVPR Workshop on Fine-Grained Visual Categorization., 2 (2021). |
| [99] | M. Y. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, et al., Meta-learning for semi-supervised few-shot classification, preprint, arXiv: 1803.00676. |
| [100] | G. Liu, L. L. Zhao, W. Li, D. S. Guo, X. Z. Fang, Class-wise Metric Scaling for Improved Few-Shot Classification, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2021), 586–595. https://doi.org/10.1109/WACV48630.2021.00063 |
Figures(13) / Tables(3)
Wu Zeng, Zheng-ying Xiao. Few-shot learning based on deep learning: A survey[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 679-711. doi: 10.3934/mbe.2024029
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