Abstract
In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives. It comprises of two surrogates, one at the architecture level to improve sample efficiency and one at the weights level, through a supernet, to improve gradient descent training efficiency. On standard benchmark datasets (C10, C100, ImageNet), the resulting models, dubbed NSGANetV2, either match or outperform models from existing approaches with the search being orders of magnitude more sample efficient. Furthermore, we demonstrate the effectiveness and versatility of the proposed method on six diverse non-standard datasets, e.g. STL-10, Flowers102, Oxford Pets, FGVC Aircrafts etc. In all cases, NSGANetV2s improve the state-of-the-art (under mobile setting), suggesting that NAS can be a viable alternative to conventional transfer learning approaches in handling diverse scenarios such as small-scale or fine-grained datasets. Code is available at https://github.com/mikelzc1990/nsganetv2.
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Notes
- 1.
Estimate from # of models evaluated by PNAS, actual sample size is not reported.
- 2.
In the supplementary material we show that better rank-order correlation at the search stage ultimately leads to finding better performing architectures.
- 3.
Due to space constraints, we report results from three datasets in the main paper and three more in the supplementary material.
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Lu, Z., Deb, K., Goodman, E., Banzhaf, W., Boddeti, V.N. (2020). NSGANetV2: Evolutionary Multi-objective Surrogate-Assisted Neural Architecture Search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12346. Springer, Cham. https://doi.org/10.1007/978-3-030-58452-8_3
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