In 2014, ILSVRC, Google published its own network known as GoogLeNet. Its performance is a little better than VGGNet; GoogLeNet's performance is 6.7% compared to VGGNet's performance of 7.3%. The main attractive feature of GoogLeNet is that it runs very fast due to the introduction of a new concept called inception module, thus reducing the number of parameters to only 5 million; that's 12 times less than AlexNet. It has lower memory use and lower power use too.

It has 22 layers, so it is a very deep network. Adding more layers increases the number of parameters and it is likely that the network overfits. There will be more computation, because a linear increase in filters results in a quadratic increase in computation. So, the designers use the inception module and GAP. The fully connected layer at the end of the network is replaced with a GAP layer because fully connected layers are generally prone to overfitting. GAP has no parameters to learn or optimize.