DC-NAS: Divide-and-Conquer Neural Architecture Search for Multi-Modal Classification
DOI:
https://doi.org/10.1609/aaai.v38i12.29281Keywords:
ML: Multi-instance/Multi-view Learning, ML: Multimodal LearningAbstract
Neural architecture search-based multi-modal classification (NAS-MMC) methods can individually obtain the optimal classifier for different multi-modal data sets in an automatic manner. However, most existing NAS-MMC methods are dramatically time consuming due to the requirement for training and evaluating enormous models. In this paper, we propose an efficient evolutionary-based NAS-MMC method called divide-and-conquer neural architecture search (DC-NAS). Specifically, the evolved population is first divided into k+1 sub-populations, and then k sub-populations of them evolve on k small-scale data sets respectively that are obtained by splitting the entire data set using the k-fold stratified sampling technique; the remaining one evolves on the entire data set. To solve the sub-optimal fusion model problem caused by the training strategy of partial data, two kinds of sub-populations that are trained using partial data and entire data exchange the learned knowledge via two special knowledge bases. With the two techniques mentioned above, DC-NAS achieves the training time reduction and classification performance improvement. Experimental results show that DC-NAS achieves the state-of-the-art results in term of classification performance, training efficiency and the number of model parameters than the compared NAS-MMC methods on three popular multi-modal tasks including multi-label movie genre classification, action recognition with RGB and body joints and dynamic hand gesture recognition.Downloads
Published
2024-03-24
How to Cite
Liang, X., Fu, P., Guo, Q., Zheng, K., & Qian, Y. (2024). DC-NAS: Divide-and-Conquer Neural Architecture Search for Multi-Modal Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13754-13762. https://doi.org/10.1609/aaai.v38i12.29281
Issue
Section
AAAI Technical Track on Machine Learning III