NSGA-Net: neural architecture search using multi-objective genetic algorithm
By Zhichao Lu, Ian Whalen, Vishnu Naresh Boddeti, Yashesh D. Dhebar, Kalyanmoy Deb, Erik D. Goodman, Wolfgang Banzhaf. 2018
544 citations recorded by the source corpus.
This paper introduces NSGA-Net --- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple and conflicting objectives, (2) an efficient procedure balancing exploration and exploitation of the space of potential neural network architectures, and (3) a procedure finding a diverse set of trade-off network architectures achieved in a single run. NSGA-Net is a population-based search algorithm that explores a space of potential neural network architectures in three steps, namely, a population initialization step that is based on prior-knowledge from hand-crafted architectures, an exploration step comprising crossover and mutation of architectures, and finally an exploitation step that utilizes the hidden useful knowledge stored in the entire history of evaluated neural architectures in the form of a Bayesian Network. Experimental results suggest that combining the dual objectives of minimizing an error metric and computational complexity, as measured by FLOPs, allows NSGA-Net to find competitive neural architectures. Moreover, NSGA-Net achieves error rate on the CIFAR-10 dataset on par with other state-of-the-art NAS methods while using orders of magnitude less computational resources. These results are encouraging and shows the promise to further use of EC methods in various deep-learning paradigms.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Quoc V. Le
- Mingxing Tan
- Luc Van Gool
- Alan L. Yuille
- Dacheng Tao
- Ruoming Pang
- Trevor Darrell
- Xiaogang Wang
Citations in the corpus
- FaPN: Feature-aligned Pyramid Network for Dense Image Prediction (2021, unknown): “Moreover, FaPN can also be easily extended to realtime semantic segmentation by pairing it with a lightweight bottom-up backbone [14, 30, 32].”
- Searching for Fast Model Families on Datacenter Accelerators (2021, methodology): “When designing fast models for inference with NAS, previous work employed multi-objective search [56, 18, 13, 29, 64, 27, 12, 21, 39, 16] to consider accuracy together with performance/efficiency.”