Abstract
Machine Learning experts spend much time on fine-tuning. A methodology that automatically searches neural architectures has been to solve this problem. Differentiable Architecture Search (DARTS) is an algorithm that solves a Neural Architecture Search problem using a gradient-based approach. We found an architecture that shows higher test accuracy than the existing DARTS architecture with the DARTS algorithm on the CIFAR-10 dataset. The architecture performed the DARTS algorithm several times and recorded the highest test accuracy of 97.62%. This result exceeds the test accuracy of 97.24 ± 0.09 shown in the existing DARTS paper. These results are expected to raise the baseline for making a practical difference in the study of Neural Architecture Search.
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Shin, J.Y., Kang, DK. (2023). A Higher Performing DARTS Model for CIFAR-10. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_10
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