Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multiple training trials. However, only partial knowledge (i.e., the measured performances of trained models and their hyperparameter configurations) from previous trials is transferred. On the other hand, Self-Distillation (SD) only transfers partial knowledge learned by the task model itself. To fully leverage the various knowledge gained from all training trials, we propose the BOSS framework, which combines BO and SD. BOSS suggests promising hyperparameter configurations through BO and carefully selects pre-trained models from previous trials for SD, which are otherwise abandoned in the conventional BO process. BOSS achieves significantly better performance than both BO and SD in a wide range of tasks including general image classification, learning with noisy labels, semi-supervised learning, and medical image analysis tasks.
@article{arxiv.2304.12666,
title = {Bayesian Optimization Meets Self-Distillation},
author = {HyunJae Lee and Heon Song and Hyeonsoo Lee and Gi-hyeon Lee and Suyeong Park and Donggeun Yoo},
journal= {arXiv preprint arXiv:2304.12666},
year = {2023}
}