HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks.

ABSTRACT: As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefull

SUBMITTER: Kim KS 

PROVIDER: S-EPMC8231656 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature