GoogLeNet Research Papers Collection with 3 Documents: Original Paper PDF, Chinese Translation PDF, Bilingual Comparison PDF

GoogLeNet, introduced by Google researchers in 2014, is a deep neural network that employs a structural innovation known as the Inception module. This module enables the network to simultaneously learn features of different sizes. The modular structure of GoogLeNet facilitates more efficient training and inference, leading to outstanding performance in image classification competitions at the time. GoogLeNet's strength lies in its ability to increase the model's depth and width while maintaining fewer parameters, thereby enhancing accuracy. Additionally, it incorporates common regularization techniques such as Dropout and L2 regularization to mitigate the risk of overfitting. These advantages position GoogLeNet as one of the most advanced image classification models of its time.