Abstract
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct few-shot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. In this paper, we propose an adaptive task sampling method to improve the generalization performance. Unlike instance based sampling, task based sampling is much more challenging due to the implicit definition of the task in each episode. Therefore, we accordingly propose a greedy class-pair based sampling method, which selects difficult tasks according to class-pair potentials. We evaluate our adaptive task sampling method on two few-shot classification benchmarks, and it achieves consistent improvements across different feature backbones, meta-learning algorithms and datasets.
C. Liu and Z. Wang—contributed equally, and completed most of this work when working at the School of Information Systems, Singapore Management University (SMU). Steven C.H. Hoi is currently with Salesforce Research Asia and on leave from SMU.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Alain, G., Lamb, A., Sankar, C., Courville, A., Bengio, Y.: Variance reduction in SGD by distributed importance sampling. arXiv preprint arXiv:1511.06481 (2015)
Allen-Zhu, Z., Qu, Z., Richtárik, P., Yuan, Y.: Even faster accelerated coordinate descent using non-uniform sampling. In: International Conference on Machine Learning, pp. 1110–1119 (2016)
Aly, M.: Survey on multiclass classification methods. Neural Netw. 19, 1–9 (2005)
Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. arXiv preprint arXiv:1810.09502 (2018)
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48. ACM (2009)
Bertinetto, L., Henriques, J.F., Torr, P.H., Vedaldi, A.: Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136 (2018)
Chang, H.S., Learned-Miller, E., McCallum, A.: Active bias: training more accurate neural networks by emphasizing high variance samples. In: Advances in Neural Information Processing Systems, pp. 1002–1012 (2017)
Chen, W., Liu, Y., Kira, Z., Wang, Y.F., Huang, J.: A closer look at few-shot classification. In: Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May (2019). https://openreview.net/forum?id=HkxLXnAcFQ
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Cross, G.R., Jain, A.K.: Markov random field texture models. IEEE Trans. Pattern Anal. Mach. Intell. 5(1), 25–39 (1983)
Csiba, D., Richtárik, P.: Importance sampling for minibatches. J. Mach. Learn. Res. 19(1), 962–982 (2018)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135 (2017). JMLR.org
Franceschi, L., Frasconi, P., Salzo, S., Grazzi, R., Pontil, M.: Bilevel programming for hyperparameter optimization and meta-learning. In: International Conference on Machine Learning, pp. 1563–1572 (2018)
Freund, Y., Schapire, R.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(771–780), 1612 (1999)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Gopal, S.: Adaptive sampling for SGD by exploiting side information. In: International Conference on Machine Learning, pp. 364–372 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Horváth, S., Richtárik, P.: Nonconvex variance reduced optimization with arbitrary sampling. arXiv preprint arXiv:1809.04146 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)
Katharopoulos, A., Fleuret, F.: Biased importance sampling for deep neural network training. arXiv preprint arXiv:1706.00043 (2017)
Katharopoulos, A., Fleuret, F.: Not all samples are created equal: deep learning with importance sampling. arXiv preprint arXiv:1803.00942 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)
Landau, B., Smith, L.B., Jones, S.S.: The importance of shape in early lexical learning. Cogn. Dev. 3(3), 299–321 (1988)
Lee, K., Maji, S., Ravichandran, A., Soatto, S.: Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10657–10665 (2019)
Li, Z., Zhou, F., Chen, F., Li, H.: Meta-SGD: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Liu, L., Zhou, T., Long, G., Jiang, J., Zhang, C.: Learning to propagate for graph meta-learning. arXiv preprint arXiv:1909.05024 (2019)
London, B.: A PAC-Bayesian analysis of randomized learning with application to stochastic gradient descent. In: Advances in Neural Information Processing Systems, pp. 2931–2940 (2017)
Loshchilov, I., Hutter, F.: Online batch selection for faster training of neural networks. arXiv preprint arXiv:1511.06343 (2015)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579–2605 (2008)
Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: Proceedings of the ICLR (2017)
Munkhdalai, T., Yuan, X., Mehri, S., Trischler, A.: Rapid adaptation with conditionally shifted neurons. In: International Conference on Machine Learning, pp. 3661–3670 (2018)
Naik, D.K., Mammone, R.J.: Meta-neural networks that learn by learning. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 1992, vol. 1, pp. 437–442. IEEE (1992)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)
Oreshkin, B., López, P.R., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: Advances in Neural Information Processing Systems, pp. 721–731 (2018)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: Proceedings of the ICLR (2016)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May (2019). https://openreview.net/forum?id=BJgklhAcK7
Satorras, V.G., Bruna, J.: Few-shot learning with graph neural networks. In: Proceedings of the ICLR (2018)
Shalev-Shwartz, S., Wexler, Y.: Minimizing the maximal loss: how and why. In: Proceedings of the ICML, pp. 793–801 (2016)
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761–769 (2016)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)
Song, H., Kim, S., Kim, M., Lee, J.G.: Ada-boundary: accelerating the DNN training via adaptive boundary batch selection (2018)
Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 403–412 (2019)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)
Thrun, S., Pratt, L.: Learning to learn: introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 3–17. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5529-2_1
Triantafillou, E., et al.: Meta-dataset: A dataset of datasets for learning to learn from few examples. arXiv preprint arXiv:1903.03096 (2019)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)
Ze, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7962–7966. IEEE (2013)
Zhang, C., Kjellstrom, H., Mandt, S.: Determinantal point processes for mini-batch diversification. arXiv preprint arXiv:1705.00607 (2017)
Zhang, C., Öztireli, C., Mandt, S., Salvi, G.: Active mini-batch sampling using repulsive point processes. Proceedings of the AAAI Conference on Artificial Intelligence 33, 5741–5748 (2019)
Zhang, R., Che, T., Ghahramani, Z., Bengio, Y., Song, Y.: MetaGAN: an adversarial approach to few-shot learning. In: Advances in Neural Information Processing Systems, pp. 2365–2374 (2018)
Zhao, P., Zhang, T.: Stochastic optimization with importance sampling for regularized loss minimization. In: International Conference on Machine Learning, pp. 1–9 (2015)
Acknowledgment
This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C., Wang, Z., Sahoo, D., Fang, Y., Zhang, K., Hoi, S.C.H. (2020). Adaptive Task Sampling for Meta-learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-58523-5_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58522-8
Online ISBN: 978-3-030-58523-5
eBook Packages: Computer ScienceComputer Science (R0)