Online Hyper-Parameter Learning for Auto-Augmentation Strategy

Chen Lin, Minghao Guo, Chuming Li, Xin Yuan, Wei Wu, Junjie Yan, Dahua Lin, Wanli Ouyang
2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  
Data augmentation is critical to the success of modern deep learning techniques. In this paper, we propose Online Hyper-parameter Learning for Auto-Augmentation (OHL-Auto-Aug), an economical solution that learns the augmentation policy distribution along with network training. Unlike previous methods on auto-augmentation that search augmentation strategies in an offline manner, our method formulates the augmentation policy as a parameterized probability distribution, thus allowing its
more » ... to be optimized jointly with network parameters. Our proposed OHL-Auto-Aug eliminates the need of re-training and dramatically reduces the cost of the overall search process, while establishes significantly accuracy improvements over baseline models. On both CIFAR-10 and ImageNet, our method achieves remarkable on search accuracy, i.e. 60× faster on CIFAR-10 and 24× faster on ImageNet, while maintaining competitive accuracies.
doi:10.1109/iccv.2019.00668 dblp:conf/iccv/LinGLYWYLO19 fatcat:phqk7plgf5h45cmini2et47mqi