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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 11: AAAI-21 Technical Tracks 11

Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm

February 1, 2023

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Authors

Akira Terauchi

Osaka Prefecture University


Naoki Mori

Osaka Prefecture University


DOI:

10.1609/aaai.v35i11.17184


Abstract:

Data augmentation is one of the most effective ways to stabilize learning by improving the generalization of machine-learning models. In recent years, automatic data augmentation methods, such as AutoAugment or Fast AutoAugment have been attracting attention; and these methods improved the results of image classification and object detection tasks. However, several problems remain. Most notably, a larger training dataset requires higher computational costs. When searching with a small dataset in an attempt to determine the data augmentation approach, the true data space and sampling data space do not fully correspond with each other, thereby causing the generalization performance to deteriorate. Moreover, in the existing automatic augmentation methods, the search phase is often dominated by an exceptional sub-policy, which results in a loss of diversity of operations. In this study, we solved these problems by introducing evolutionary computation to previous methods. As mentioned earlier, maintaining diversity is essential. Therefore, we adopted the thermodynamical genetic algorithm (TDGA), which can control the population diversity with a specific genetic operator, known as the thermodynamical selection rule. To confirm the effectiveness of the proposed method, computational experiments were conducted using two benchmark datasets, CIFAR-10 and SVHN, as examples. The experimental results show that the proposed method can obtain various useful augmentation sub-policies for the problems while reducing the computational cost.

Topics: AAAI

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HOW TO CITE:

Akira Terauchi||Naoki Mori Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm Proceedings of the AAAI Conference on Artificial Intelligence (2021) 9851-9858.

Akira Terauchi||Naoki Mori Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm AAAI 2021, 9851-9858.

Akira Terauchi||Naoki Mori (2021). Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm. Proceedings of the AAAI Conference on Artificial Intelligence, 9851-9858.

Akira Terauchi||Naoki Mori. Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.9851-9858.

Akira Terauchi||Naoki Mori. 2021. Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm. "Proceedings of the AAAI Conference on Artificial Intelligence". 9851-9858.

Akira Terauchi||Naoki Mori. (2021) "Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm", Proceedings of the AAAI Conference on Artificial Intelligence, p.9851-9858

Akira Terauchi||Naoki Mori, "Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm", AAAI, p.9851-9858, 2021.

Akira Terauchi||Naoki Mori. "Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.9851-9858.

Akira Terauchi||Naoki Mori. "Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 9851-9858.

Akira Terauchi||Naoki Mori. Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm. AAAI[Internet]. 2021[cited 2023]; 9851-9858.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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