Abstract
Image classification algorithms are commonly based on the Independent and Identically Distribution (IID) assumption, but in practice, the Out-Of-Distribution (OOD) problem is widely existing, i.e., the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the IID assumption are limiting generalization. Causal inference is an important method to enhance the out-of-distribution generalization of models by partitioning various contexts from data and leading models to learn context-invariant predictions in different situations. However, existing methods mostly have imbalance problems due to the lack of constraints when partitioning data, which weakens the improvement of generalization. Therefore, we propose a Balanced Partition Causal Inference (BP-Causal) method, which automatically generates fine-grained balanced data partitions in an unsupervised manner, thereby enhancing the generalization ability of models in different contexts. Experiments on the OOD datasets NICO and NICO++ demonstrate that BP-Causal achieves stable predictions on OOD data, and we also find that models using BP-Causal focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalization ability.
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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., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (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)
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Geirhos, R., et al.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)
Gong, M., et al.: Domain adaptation with conditional transferable components. In: International Conference on Machine Learning, pp. 2839–2848. PMLR (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)
He, Y., Shen, Z., Cui, P.: Towards Non-IID image classification: a dataset and baselines. Pattern Recognit. 110, 107383 (2021)
Heinze-Deml, C., Peters, J., Meinshausen, N.: Invariant causal prediction for nonlinear models. J. Causal Inference 6(2) (2018)
Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)
Jin, W., Barzilay, R., Jaakkola, T.: Domain extrapolation via regret minimization. arXiv preprint arXiv:2006.03908 (2020)
Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A., Togneri, R.: Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 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)
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, D., et al.: Exploring the limits of weakly supervised pretraining. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 185–201. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_12
Muandet, K., Balduzzi, D., Schölkopf, B.: Domain generalization via invariant feature representation. In: International Conference on Machine Learning, pp. 10–18. PMLR (2013)
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)
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: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 467–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_29
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)
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: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Xu R., Yu, H., Shen, Z., Cui, P., Zhang, X., He, Y.: Nico++: towards better benchmarking for domain generalization (2022)
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., Zhang, A.: A survey on causal inference. ACM Trans. Knowl. Discov. Data (TKDD) 15(5), 1–46 (2021)
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
Acknowledgments
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., Li, X., Ma, H., Qi, Z., Meng, X., Meng, L. (2022). Causal Inference with Sample Balancing for Out-of-Distribution Detection in Visual Classification. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13604. Springer, Cham. https://doi.org/10.1007/978-3-031-20497-5_47
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