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BO-Aug: learning data augmentation policies via Bayesian optimization

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Abstract

Data augmentation has been an essential technique to increase the amount and diversity of datasets, thus improving deep learning models. To pursue further performance, several automated data augmentation approaches have recently been proposed to find data augmentation policies automatically. However, there are still some key issues that deserve further exploration, i.e., a precise policy search space definition, the instructive policy evaluation method, and the low computational cost of policy search. In this paper, we propose a novel method named BO-Aug that attempts to solve the above issues. Empirical verification on three widely used image classification datasets shows that the proposed method can achieve state-of-the-art or comparable performance compared with advanced automated data augmentation methods, with a relatively low cost. Our code is available at https://github.com/Zhangcx19/BO-Aug.

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Notes

  1. https://pillow.readthedocs.io/en/stable/

  2. https://tensorflow.google.cn/

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant Nos. 2021ZD0112501 and 2021ZD0112502; the National Natural Science Foundation of China under Grant Nos. 62172185 and 61876069; Jilin Province Key Scientific and Technological Research and Development Project under Grant Nos. 20180201067GX and 20180201044GX; Jilin Province Natural Science Foundation under Grant No. 20200201036JC; and China Postdoctoral Science Foundation funded project under Grant No.2021M701388.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, No.2699, Qianjin Street, Chaoyang District, Changchun City, 130012, Jilin Province, China

    Chunxu Zhang, Ximing Li, Zijian Zhang, Jiaxu Cui & Bo Yang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No.2699, Qianjin Street, Chaoyang District, Changchun City, 130012, Jilin Province, China

    Chunxu Zhang, Ximing Li, Zijian Zhang, Jiaxu Cui & Bo Yang

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  1. Chunxu Zhang
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  2. Ximing Li
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  3. Zijian Zhang
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  4. Jiaxu Cui
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  5. Bo Yang
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Correspondence to Bo Yang.

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Appendices

Appendix A: Image transformation operations used for DA policies search

All available image transformation functions during the search process are listed below. These functions accept an image and corresponding operation parameters as input, and output a transformed image. The range of magnitudes for each operation is shown in the third column. Some transformations do not use magnitude information (e.g., Invert and Equalize).

Table 8 Image transformation operations used for DA policies search
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Appendix B: Mapping relationship between operation type value of policy vector and operation types

For each policy, we regard its first dimension as the two DA operation types. Considering that there are 14 DA operations in total, we use a number between 0 and 196 to represent two operations. Here we list the mapping relationship of the value and corresponding operation types.

Table 9 Mapping relationship between operation type value of policy vector and operation types
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Appendix C: Hyperparameter configuration in the experiments

Once BO-Aug finds the optimal DA policies, we will validate the policies’ performance on different target models based on several datasets. Here we give the learning rate and weight decay values during target model training.

Table 10 Hyperparameter configuration in the experiments
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Appendix D: Policies found on reduced CIFAR-10

Table 11 Policies found on reduced CIFAR-10
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Appendix E: Policies found on reduced SVHN

Table 12 Policies found on reduced SVHN
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Appendix F: Policies found on reduced TinyImagenet

Table 13 Policies found on reduced TinyImagenet
Full size table

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Zhang, C., Li, X., Zhang, Z. et al. BO-Aug: learning data augmentation policies via Bayesian optimization. Appl Intell 53, 8978–8993 (2023). https://doi.org/10.1007/s10489-022-03790-z

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