Abstract
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing work focuses on constructing a unified policy applicable to all data samples in a given dataset, without considering sample or class variations. In this paper, we propose a novel two-stage data augmentation algorithm, named Label-Aware AutoAugment (LA3), which takes advantage of the label information, and learns augmentation policies separately for samples of different labels. LA3 consists of two learning stages, where in the first stage, individual augmentation methods are evaluated and ranked for each label via Bayesian Optimization aided by a neural predictor, which allows us to identify effective augmentation techniques for each label under a low search cost. And in the second stage, a composite augmentation policy is constructed out of a selection of effective as well as complementary augmentations, which produces significant performance boost and can be easily deployed in typical model training. Extensive experiments demonstrate that LA3 achieves excellent performance matching or surpassing existing methods on CIFAR-10 and CIFAR-100, and achieves a new state-of-the-art ImageNet accuracy of \(79.97\%\) on ResNet-50 among auto-augmentation methods, while maintaining a low computational cost.
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References
Cubuk, E.D., Zoph, B., Mané, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation strategies from data. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 113–123. IEEE (2019)
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Gastaldi, X.: Shake-shake regularization. arXiv preprint arXiv:1705.07485 (2017)
Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5927–5935 (2017)
Hataya, R., Zdenek, J., Yoshizoe, K., Nakayama, H.: Faster autoaugment: learning augmentation strategies using backpropagation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 1–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_1
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)
Ho, D., Liang, E., Chen, X., Stoica, I., Abbeel, P.: Population based augmentation: efficient learning of augmentation policy schedules. In: International Conference on Machine Learning, pp. 2731–2741. PMLR (2019)
Inoue, H.: Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929 (2018)
Jaderberg, M., et al.: Population based training of neural networks. arXiv preprint arXiv:1711.09846 (2017)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)
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 (2009)
Li, Y., Hu, G., Wang, Y., Hospedales, T., Robertson, N.M., Yang, Y.: Differentiable automatic data augmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 580–595. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_35
Lim, S., Kim, I., Kim, T., Kim, C., Kim, S.: Fast autoaugment. Adv. Neural. Inf. Process. Syst. 32, 6665–6675 (2019)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Tian, K., Lin, C., Sun, M., Zhou, L., Yan, J., Ouyang, W.: Improving auto-augment via augmentation-wise weight sharing. Adv. Neural. Inf. Process. Syst. 33, 19088–19098 (2020)
Wang, Y., et al.: Fine-grained autoaugmentation for multi-label classification. arXiv preprint arXiv:2107.05384 (2021)
Wen, W., Liu, H., Chen, Y., Li, H., Bender, G., Kindermans, P.-J.: Neural predictor for neural architecture search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 660–676. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_39
White, C., Neiswanger, W., Savani, Y.: Bananas: Bayesian optimization with neural architectures for neural architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 10293–10301 (2021)
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)
Zagoruyko, S., Komodakis, N.: Wide residual networks. In: British Machine Vision Conference 2016. British Machine Vision Association (2016)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)
Zhang, X., Wang, Q., Zhang, J., Zhong, Z.: Adversarial autoaugment. In: International Conference on Learning Representations (2019)
Zhou, F., Li, J., Xie, C., Chen, F., Hong, L., Sun, R., Li, Z.: Metaaugment: sample-aware data augmentation policy learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11097–11105 (2021)
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Zhao, M., Lu, S., Wang, Z., Wang, X., Niu, D. (2022). LA3: Efficient Label-Aware AutoAugment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_16
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