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Meta-learning with normalized projection loss reweighting for webly supervised fine-grained recognition

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Abstract

Clean data is critical to the success of deep learning training. In practice, noisy labels are often included in the data. When the network learns these noisy labels, it will cause the model to degrade. In order to alleviate this problem, the sample reweighting method based on meta-learning has been proposed in recent years. It can reduce the negative impact of noisy labels by adjusting the model’s learning degree to the sample. It also make the model pay more attention to the cleaner data in the dataset. Based on this, we propose Meta-Learning with Normalized Projection Loss Reweighting (MLNP), which is a sample reweighting method based on meta learning. This strategy directs the classification network to identify potentially clean data in the dataset with a higher weight. The weight is based on the Euclidean distance between features and projection similarity of the meta set sample and the training set sample. Furthermore, learning data with less weight is more likely to be noisy. Through experiments, we show the robustness of MLNP and achieves advanced performance on a range of datasets.

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Data availibility

The WebFG-496, CUB200-2011, FGVC-aircraft, Stanford Cars datasets are provided in Table 1 taken from [1,2,3,4].

References

  1. Sun, Z., Yao, Y., Wei, X.-S., Zhang, Y., Shen, F., Wu, J., Zhang, J., Shen, H.T.: Webly supervised fine-grained recognition: benchmark datasets and an approach. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10602–10611 (2021)

  2. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)

  3. Maji, S., Rahtu, E., Kannala, J., Blaschko, M.B., Vedaldi, A.: Fine-grained visual classification of aircraft. CoRR abs/1306.5151 (2013)

  4. Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: 2013 IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013). https://doi.org/10.1109/ICCVW.2013.77

  5. Li, D., Li, X., Wang, B.: Texture direction recognition of wooden beams and columns based on improved meta-learning. Signal, Image Video Process. 17(8), 4447–4454 (2023). https://doi.org/10.1007/s11760-023-02678-w

    Article  MathSciNet  MATH  Google Scholar 

  6. Zhao, Z., Chen, G., Lin, Y.: Temporal-masked skeleton-based action recognition with supervised contrastive learning. Signal Image Video Process. 17(5), 2267–2275 (2023)

    Article  MATH  Google Scholar 

  7. Fu, B., Dong, Y., Fu, S., Wu, Y., Ren, Y., Thanh, D.N.H.: Multistage supervised contrastive learning for hybrid-degraded image restoration. Signal, Image Video Process. 17, 573–581 (2023). https://doi.org/10.1007/s11760-022-02262-8

    Article  MATH  Google Scholar 

  8. Dong, H., Zhang, T., Zhang, T., Wei, L.: Supervised learning-based retinal vascular segmentation by m-unet full convolutional neural network. Signal, Image Video Process. 16, 1755–1761 (2022). https://doi.org/10.1007/s11760-022-02132-3

    Article  MATH  Google Scholar 

  9. Lin, C., Mao, X., Qiu, C., Zou, L.: Dtcnet: transformer-cnn distillation for super-resolution of remote sensing image. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17, 11117–11133 (2024). https://doi.org/10.1109/JSTARS.2024.3409808

    Article  MATH  Google Scholar 

  10. Ye, A., Xiao, X., Xiao, H., Jiang, C., Lin, C.: Acgnd: towards lower complexity and fast solution for dynamic tensor inversion. Complex Intell. Syst. 31, 1–15 (2024)

    MATH  Google Scholar 

  11. Lin, C., Qiu, C., Jiang, H., Zou, L.: A deep neural network based on prior-driven and structural preserving for SAR image despeckling. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16, 6372–6392 (2023). https://doi.org/10.1109/JSTARS.2023.3292325

    Article  Google Scholar 

  12. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64(3), 107–115 (2021). https://doi.org/10.1145/3446776

    Article  MATH  Google Scholar 

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv: Learning, (2015)

  14. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  MATH  Google Scholar 

  15. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. Learning, Learning (2017)

  16. Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2691–2699 (2015). https://doi.org/10.1109/CVPR.2015.7298885

  17. Chen, X., Gupta, A.: Webly supervised learning of convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1431–1439 (2015). https://doi.org/10.1109/ICCV.2015.168

  18. Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R.: Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080 (2014)

  19. Liu, T., Tao, D.: Classification with noisy labels by importance reweighting. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 447–461 (2016). https://doi.org/10.1109/TPAMI.2015.2456899

    Article  MATH  Google Scholar 

  20. Zhang, H., Xing, X., Liu, L.: Dualgraph: a graph-based method for reasoning about label noise. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https://doi.org/10.1109/cvpr46437.2021.00953

  21. Shu, J., Xie, Q., Yi, L., Zhao, Q., Zhou, S., Xu, Z., Meng, D.: Meta-weight-net: learning an explicit mapping for sample weighting. Neural Information Processing Systems, Neural Information Processing Systems (2019)

  22. Belton, N., Hagos, M.T., Lawlor, A., Curran, K.M.: Fewsome: One-class few shot anomaly detection with siamese networks. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2978–2987 (2023). https://doi.org/10.1109/CVPRW59228.2023.00299

  23. Yue, C., Huang, R., Towey, D., Xian, Z., Wu, G.: An entropy-based group decision-making approach for software quality evaluation. Expert. Syst. Appl. 238, 121979 (2024)

    Article  Google Scholar 

  24. Yue, C., Huang, R., Towey, D., Xian, Z., Wu, G.: An entropy-based group decision-making approach for software quality evaluation. Expert. Syst. Appl. 238, 121979 (2024). https://doi.org/10.1016/j.eswa.2023.121979

    Article  Google Scholar 

  25. Oza, P., Patel, V.M.: One-class convolutional neural network. IEEE Signal Process. Lett. 26, 277–281 (2019). https://doi.org/10.1109/lsp.2018.2889273

    Article  MATH  Google Scholar 

  26. Jewell, J., Khazaie, V., Mohsenzadeh, Y.: Oled: one-class learned encoder-decoder network with adversarial context masking for novelty detection. arXiv: Computer Vision and Pattern Recognition (2021)

  27. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv: Computer Vision and Pattern Recognition (2020)

  28. Cohen, M., Avidan, S.: Transformaly–two (feature spaces) are better than one

  29. Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: self-supervised learning for anomaly detection and localization. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021). https://doi.org/10.1109/cvpr46437.2021.00954

  30. Wang, G., Wang, Y., Qin, J., Zhang, D., Bao, X., Huang, D.: Unilaterally aggregated contrastive learning with hierarchical augmentation for anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6888–6897 (2023)

  31. Yue, Z., Jia, Y.: A direct projection-based group decision-making methodology with crisp values and interval data. Soft Comput. 21, 2395–2405 (2017). https://doi.org/10.1007/s00500-015-1953-5

    Article  MATH  Google Scholar 

  32. Wang, Q.: A two-tuple linguistics muti-attribute group decision making method based on bi-directional projection operator. Mathematics in Practice and Theory (2015)

  33. Fu, C., Gao, X., Liu, M., Liu, X., Han, L., Chen, J.: Grap: grey risk assessment based on projection in ad hoc networks. J. Parallel Distrib. Comput. 71(9), 1249–1260 (2011). https://doi.org/10.1016/j.jpdc.2010.11.012

    Article  MATH  Google Scholar 

  34. Zheng, G., Jing, Y., Huang, H., Gao, Y.: Application of improved grey relational projection method to evaluate sustainable building envelope performance. Appl. Energy 87(2), 710–720 (2010). https://doi.org/10.1016/j.apenergy.2009.08.020

    Article  MATH  Google Scholar 

  35. Xu, Z., Hu, H.: Projection models for intuitionistic fuzzy multiple attribute decision making. Int. J. Inf. Technol. Decis. Mak. 09(02), 267–280 (2010). https://doi.org/10.1142/s0219622010003816

    Article  MATH  Google Scholar 

  36. Shu, J., Yuan, X., Meng, D., Xu, Z.: Cmw-net: learning a class-aware sample weighting mapping for robust deep learning. CoRR abs/2202.05613 (2022) 2202.05613

  37. Yi, M., Hou, L., Shang, L., Jiang, X., Li, Q., Ma, Z.: Reweighting augmented samples by minimizing the maximal expected loss. International Conference on Learning Representations,International Conference on Learning Representations (2021)

  38. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

  39. Shu, J., Yuan, X., Meng, D., Xu, Z.: Cmw-net: learning a class-aware sample weighting mapping for robust deep learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(10), 11521–11539 (2023). https://doi.org/10.1109/TPAMI.2023.3271451

    Article  MATH  Google Scholar 

  40. 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)

  41. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  42. Lin, T.-Y., RoyChowdhury, A., Maji, S.: Bilinear cnn models for fine-grained visual recognition. In: 2015 IEEE International Conference on Computer Vision (ICCV) (2015). https://doi.org/10.1109/iccv.2015.170

  43. Dubey, A., Gupta, O., Guo, P., Raskar, R., Farrell, R., Naik, N.: Pairwise confusion for fine-grained visual classification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 70–86 (2018)

  44. Zhang, C., Lin, G., Wang, Q., Shen, F., Yao, Y., Tang, Z.: Guided by meta-set: a data-driven method for fine-grained visual recognition. IEEE Trans. Multimed. 25, 4691–4703 (2022)

    Article  MATH  Google Scholar 

  45. Liu, Y., Wu, Z., Lo, S.-L., Chen, Z., Ke, G., Yue, C.: Data reweighting net for web fine-grained image classification. Multimed. Tools Appl. 2, 1–21 (2024)

    MATH  Google Scholar 

  46. Malach, E., Shalev-Shwartz, S.: Decoupling“when to update”from“how to update”. Advances in neural information processing systems 30 (2017)

  47. Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M.: Co-teaching: robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31 (2018)

  48. Shu, J., Xie, Q., Yi, L., Zhao, Q., Zhou, S., Xu, Z., Meng, D.: Meta-weight-net: learning an explicit mapping for sample weighting. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 1917–1928 (2019). https://proceedings.neurips.cc/paper/2019/hash/e58cc5ca94270acaceed13bc82dfedf7-Abstract.html

  49. Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. arXiv: Learning (2018)

  50. Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. International Conference on Machine Learning, (2018)

  51. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)

  52. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  53. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

  54. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks, pp. 630–645 (2016). https://doi.org/10.1007/978-3-319-46493-0_38

  55. Malach, E., Shalev-Shwartz, S.: Decoupling when to update from how to update. Neural Information Processing Systems, (2017)

  56. Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M.: Co-teaching: robust training of deep neural networks with extremely noisy labels. Neural Information Processing Systems, (2018) https://doi.org/10.5555/3327757.3327944

  57. Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944–1952 (2017)

  58. Yue, C.: Picture fuzzy normalized projection and extended vikor approach to software reliability assessment. Appl. Soft Comput. 88, 106056 (2020). https://doi.org/10.1016/j.asoc.2019.106056

    Article  MATH  Google Scholar 

  59. Yue, C.: A projection-based approach to software quality evaluation from the users’perspectives. Int. J. Mach. Learn. Cybern. 10, 2341–2353 (2018)

    Article  MATH  Google Scholar 

  60. Yue, C.: Projection-based approach to group decision-making with hybrid information representations and application to software quality evaluation. Comput. Ind. Eng. 132, 98–113 (2019)

    Article  MATH  Google Scholar 

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Liu: Designed and wrote of full paper experiments, images and tables, text. Yue: Proposed Normalized Projection idea and Reviewed the paper. Lo: Supervisor. Reviewed the paper and proposed revision suggestions. Wu: Proposed suggestions for the writing of ablation experiments. Deng: Proposed text writing and idea suggestions.

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Correspondence to Sio-long Lo.

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Liu, Y., Yue, C., Lo, Sl. et al. Meta-learning with normalized projection loss reweighting for webly supervised fine-grained recognition. SIViP 19, 67 (2025). https://doi.org/10.1007/s11760-024-03591-6

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