Comparative Analysis of Deep learning Models for Various Optimizer Embedded with Gradient Centralization - Agarwal, Vertika; Lohani, M. C.; Bist, Ankur Singh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Gradient Centralization (GC)emerges out an powerful optimization technique in area of Deep Convolutional neural network. It shows remarkable improvement in the execution time of deep learning models and opens up the scope of analyzing gradient vector to improve optimizer performance. It directly works upon the gradients and centralizes the gradient vector to have zero mean. One of the key factors which drives the attention of researchers is its embedding factor which allow its functionality to be explored with existing DNN optimizer. Our research works draws out individual and comparative analysis of GC embedded with RMS prop (Root Mean Square Propagation), Adam, Adagrad and Adadelta for three deep learning models: Mobile net, Nasnet and Densenet 201. Experiments are carried out with lung disease dataset. Highly motivating results are achieved through this embedding and accuracy of models has been enhanced up to 99%. Improved trends are also projected for Loss and Execution time.