Abstract
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features. However, conventional methods apply causal learners from multiple data splits, which may incur biased representation learning from imbalanced data distributions and difficulty in invariant feature learning from heterogeneous sources. To address these issues, this paper presents a balanced meta-causal learner (BMCL), which includes a balanced task generation module (BTG) and a meta-causal feature learning module (MCFL). Specifically, the BTG module learns to generate balanced subsets by a self-learned partitioning algorithm with constraints on the proportions of sample classes and contexts. The MCFL module trains a meta-learner adapted to different distributions. Experiments conducted on NICO++ dataset verified that BMCL effectively identifies the class-invariant visual regions for classification and may serve as a general framework to improve the performance of the state-of-the-art methods.
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References
Achille, A., Soatto, S.: Information dropout: Learning optimal representations through noisy computation. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2897–2905 (2018)
Ahuja, K., Shanmugam, K., Varshney, K., Dhurandhar, A.: Invariant risk minimization games. In: International Conference on Machine Learning. pp. 145–155. PMLR (2020)
Arjovsky, M.: Out of distribution generalization in machine learning. Ph.D. thesis, New York University (2020)
Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)
Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: Towards domain generalization using meta-regularization. Advances in neural information processing systems 31 (2018)
Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., Pal, C.: A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912 (2019)
Clark, C., Yatskar, M., Zettlemoyer, L.: Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases. arXiv preprint arXiv:1909.03683 (2019)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. pp. 1126–1135. PMLR (2017)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)
Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4367–4375 (2018)
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: International Conference on Machine Learning. pp. 2376–2384. PMLR (2019)
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)
He, Y., Shen, Z., Cui, P.: Towards non-iid image classification: A dataset and baselines. Pattern Recogn. 110, 107383 (2021)
Heinze-Deml, C., Peters, J., Meinshausen, N.: Invariant causal prediction for nonlinear models. Journal of Causal Inference 6(2) (2018)
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al.: The many faces of robustness: A critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8340–8349 (2021)
Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)
Hospedales, T.M., Antoniou, A., Micaelli, P., Storkey, A.J.: Meta-learning in neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence (2021)
Jin, W., Barzilay, R., Jaakkola, T.: Domain extrapolation via regret minimization. arXiv preprint arXiv:2006.03908 (2020)
Jin, X., Lan, C., Zeng, W., Chen, Z.: Style normalization and restitution for domain generalization and adaptation. IEEE Transactions on Multimedia (2021)
Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE transactions on neural networks and learning systems 29(8), 3573–3587 (2017)
Kim, B., Kim, H., Kim, K., Kim, S., Kim, J.: Learning not to learn: Training deep neural networks with biased data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9012–9020 (2019)
Kim, H., Shin, S., Jang, J., Song, K., Joo, W., Kang, W., Moon, I.C.: Counterfactual fairness with disentangled causal effect variational autoencoder. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 8128–8136 (2021)
Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)
Mahajan, Dhruv, Girshick, Ross, Ramanathan, Vignesh, He, Kaiming, Paluri, Manohar, Li, Yixuan, Bharambe, Ashwin, van der Maaten, Laurens: Exploring the limits of weakly supervised pretraining. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11206, pp. 185–201. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_12
Meinshausen, N., Bühlmann, P.: Maximin effects in inhomogeneous large-scale data. Ann. Stat. 43(4), 1801–1830 (2015)
Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Basic books (2018)
Peters, J., Bühlmann, P., Meinshausen, N.: Causal inference by using invariant prediction: identification and confidence intervals. J. R. Stat. Soc. Ser B (Stat. Methodol. 78(5), 947–1012 (2016)
Pfister, N., Bühlmann, P., Peters, J.: Invariant causal prediction for sequential data. J. Am. Stat. Assoc. 114(527), 1264–1276 (2019)
Ren, M., Liao, R., Fetaya, E., Zemel, R.: Incremental few-shot learning with attention attractor networks. In: 32nd Advances in Neural Information Processing Systems (2019)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: European Conference on Computer Vision, pp. 467–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_29
Shen, Z., Cui, P., Kuang, K., Li, B., Chen, P.: Causally regularized learning with agnostic data selection bias. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 411–419 (2018)
Shen, Z., et al.: Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624 (2021)
Subbaswamy, A., Saria, S.: Counterfactual normalization: proactively addressing dataset shift and improving reliability using causal mechanisms. arXiv preprint arXiv:1808.03253 (2018)
Tang, K., Huang, J., Zhang, H.: Long-tailed classification by keeping the good and removing the bad momentum causal effect. Adv. Neural. Inf. Process. Syst. 33, 1513–1524 (2020)
Tseng, H.Y., Lee, H.Y., Huang, J.B., Yang, M.H.: Cross-domain few-shot classification via learned feature-wise transformation. arXiv preprint arXiv:2001.08735 (2020)
Wang, J., et al.: Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering (2022)
Wang, R., Yi, M., Chen, Z., Zhu, S.: Out-of-distribution generalization with causal invariant transformations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 375–385 (2022)
Wang, T., Zhou, C., Sun, Q., Zhang, H.: Causal attention for unbiased visual recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3091–3100 (2021)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Xu, Y., Li, Y.L., Li, J., Lu, C.: Constructing balance from imbalance for long-tailed image recognition. arXiv preprint arXiv:2208.02567 (2022)
Yue, Z., Sun, Q., Hua, X.S., Zhang, H.: Transporting causal mechanisms for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8599–8608 (2021)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., Liu, H.: Nico++: Towards better benchmarking for domain generalization. ArXiv abs/2204.08040 (2022)
Acknowledgment
This work is supported in part by the Excellent Youth Scholars Program of Shandong Province (Grant no. 2022HWYQ-048) and the Oversea Innovation Team Project of the “20 Regulations for New Universities" funding program of Jinan (Grant no. 2021GXRC073)
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Wang, Y. et al. (2023). Meta-Causal Feature Learning for Out-of-Distribution Generalization. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_36
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