GoogLeDNet: GoogLeNet with Dendritic Learning for Image Classification

The neuron model is a widely-used approach for classification problems that imitates the behavior of neurons in the brain. However, most existing neuron models do not account for the non-linear characteristics of dendrites and synapses. To address this limitation, we propose a novel deep learning model, called GoogLeDNet, which combines the GoogLeNet architecture with the dendritic neuron model (DNM). By incorporating the non-linear characteristics of dendrites and synapses, our model closely resembles the structure of biological neurons and demonstrates superior performance on the CelebA gender classification dataset. Compared to other baseline models, including AlexNet, MobileNet, ShuffleNet, and GoogLeNet, our model achieves an accuracy of 93.3% and an F1 score of 93.2%.