Abstract
Few-shot learning aims to mitigate the need for large-scale annotated data in the real world. The focus of few-shot learning is how to quickly adapt to unseen tasks, which heavily depends on outstanding feature extraction ability. Motivated by the success of self-supervised learning, we propose a novel multi-task self-supervised learning framework for few-shot learning. To alleviate the deficiency of annotated samples in few-shot classification tasks, we introduce and analyze three different aspects, i.e., data augmentation, feature discrimination, and generalization, to improve the ability of feature learning. The proposed method achieves clear classification boundaries for different categories and shows promising generalization ability. Experimental results demonstrate that our method outperforms the state-of-the-arts on four few-shot classification benchmarks.
This work is supported in part by the National Natural Science Foundation of China Under Grants No. U20B2066, the Open Research Projects of Zhejiang Lab (No. 2021KB0AB01), and the National Key R&D Program of China (Grant No. 2020AAA0109304).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bertinetto, L., Henriques, J.F., Torr, P.H.S., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. ArXiv arxiv:1805.08136 (2019)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. ArXiv arxiv:2002.05709 (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)
Dhillon, G.S., Chaudhari, P., Ravichandran, A., Soatto, S.: A baseline for few-shot image classification. arXiv (2019)
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1422–1430 (2015)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)
Gidaris, S., Bursuc, A., Komodakis, N., Pérez, P., Cord, M.: Boosting few-shot visual learning with self-supervision. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. ArXiv arxiv:1803.07728 (2018)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Hinton, G.E.: Visualizing high-dimensional data using t-SNE. Vigiliae Christianae 9, 2579–2605 (2008)
Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. ArXiv arxiv:1503.02531 (2015)
Khosla, P., et al.: Supervised contrastive learning. ArXiv arxiv:2004.11362 (2020)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition (2015)
Lee, H., Hwang, S.J., Shin, J.: Rethinking data augmentation: Self-supervision and self-distillation. ArXiv arxiv:1910.05872 (2019)
Zhang, C., Cai, Y., Lin, G., Shen, C.: DeepEMD: Differentiable Earth Mover’s Distance for Few-Shot Learning (2020)
Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10649–10657 (2019)
Mobahi, H., Farajtabar, M., Bartlett, P.: Self-distillation amplifies regularization in Hilbert space. ArXiv arxiv:2002.05715 (2020)
Oreshkin, B.N., Rodriguez, P., Lacoste, A.: Tadam: task dependent adaptive metric for improved few-shot learning. In: NeurIPS (2020)
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536–2544 (2016)
Raghu, A., Raghu, M., Bengio, S., Vinyals, O.: Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. ArXiv arxiv:1909.09157 (2020)
Rajasegaran, J., Khan, S., Hayat, M., Khan, F., Shah, M.: Self-supervised knowledge distillation for few-shot learning. ArXiv arxiv:2006.09785 (2020)
Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. ArXiv arxiv:1803.00676 (2018)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NIPS (2017)
Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? ArXiv arxiv:2003.11539 (2020)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: NIPS (2016)
Wertheimer, D., Tang, L., Hariharan, B.: Few-shot classification with feature map reconstruction networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Ye, H.J., Hu, H., Zhan, D.C., Sha, F.: Few-shot learning via embedding adaptation with set-to-set functions. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation (2019)
Zhang, M., Zhang, J., Lu, Z., Xiang, T., Ding, M., Huang, S.: IEPT: instance-level and episode-level pretext tasks for few-shot learning. In: ICLR (2021)
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, F., Wang, R., Zhang, S., Cao, X. (2021). Few-Shot Classification with Multi-task Self-supervised Learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-92273-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92272-6
Online ISBN: 978-3-030-92273-3
eBook Packages: Computer ScienceComputer Science (R0)