Effectiveness of Optimization Algorithms in Deep Image Classification
This is an incremental study evaluating optimization algorithms for deep learning practitioners in image classification.
The paper compares the performance of Adam, AdaBelief, Padam, and SGD+Momentum optimizers on AlexNet, VGGNet, and ResNet using the EMNIST dataset for image classification, but does not report specific numerical results.
Adam is applied widely to train neural networks. Different kinds of Adam methods with different features pop out. Recently two new adam optimizers, AdaBelief and Padam are introduced among the community. We analyze these two adam optimizers and compare them with other conventional optimizers (Adam, SGD + Momentum) in the scenario of image classification. We evaluate the performance of these optimization algorithms on AlexNet and simplified versions of VGGNet, ResNet using the EMNIST dataset. (Benchmark algorithm is available at \hyperref[https://github.com/chuiyunjun/projectCSC413]{https://github.com/chuiyunjun/projectCSC413}).