arXiv 2108.056132021-08-12cs.CV

Cascade Bagging for Accuracy Prediction with Few Training Samples

Hikvision Research Institute（海康威视研究院）；Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）

Ruyi Zhang, Ziwei Yang, Zhi Yang, Xubo Yang, Lei Wang, Zheyang Li

英文摘要：

Accuracy predictor is trained to predict the validation accuracy of an network from its architecture encoding. It can effectively assist in designing networks and improving Neural Architecture Search(NAS) efficiency. However, a high-performance predictor depends on adequate trainning samples, which requires unaffordable computation overhead. To alleviate this problem, we propose a novel framework to train an accuracy predictor under few training samples. The framework consists ofdata augmentation methods and an ensemble learning algorithm. The data augmentation methods calibrate weak labels and inject noise to feature space. The ensemble learning algorithm, termed cascade bagging, trains two-level models by sampling data and features. In the end, the advantages of above methods are proved in the Performance Prediciton Track of CVPR2021 1st Lightweight NAS Challenge. Our code is made public at: https://github.com/dlongry/Solutionto-CVPR2021-NAS-Track2.

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