Robust Optimization of Convolution Natural Network
Chongjie Ye · Journal of Physics Conference Series · 2020
Abstract Deep learning has played a very important role in computer vision. However, most of the methods used in computer vision highly rely on human to adjust the hyperparameter. That takes researchers lots of time, but the results sometime could not be most optimized. Besides, many architectures cannot perform robustly in training with noised data. This essay aims to solve the hyperparameter optimization problem by adapting the fruit fly optimization algorithm and suppose a high robust Convolution Natural Network including a Gaussian filter. Compared with methods such as FaceNe, InceptionV3 and Resnet5, GauCNN perform higher efficiency and accuracy with noise data.