[lecture11][0606] Inquiry About the Role of Auxiliary Fully Connected Layers in GoogleNet

[zxcv3296]

I am writing to inquire about the specific ways in which the auxiliary fully connected (FC) layers in GoogleNet help mitigate the gradient vanishing problem during training. The gradient vanishing issue is a significant challenge in deep neural network training, where gradients become progressively smaller as they are backpropagated through the layers, leading to slow learning or even a complete halt in learning for the earlier layers.

Could you please elaborate on how these auxiliary FC layers address this problem within the context of GoogleNet? I am particularly interested in understanding the mechanisms by which these layers influence the backpropagation process, the strategic placement of these layers within the network, and any additional benefits they provide beyond mitigating the gradient vanishing issue.

Thank you.

[yjyoo3312]

@zxcv3296 Thank you for commenting the good point!

Auxiliary losses are same as the primary classification loss, but they are applied to intermediate feature layers. While the gradients from the original loss diminish as they propagate toward the initial layers, the gradients from the auxiliary loss extend further back. (It is assumed that gradients calculated from the same loss will be beneficial to each other, even when applied to features at different locations.)