I am trying to understand the two common approaches of transfer learing. I am reading this: Transfer Learning for Computer Vision Tutorial — PyTorch Tutorials 1.9.0+cu102 documentation.
I am wondering what is the role of pre-trained weights(like ImageNet) in “ConvNet as fixed feature extractor” approach? Say, I am using resnet-18 (pre-trained on ImageNet), freezing all the convolutional layers in the network and keeping the last fully connected layer which is trained with random weight initialization(as stated in the given link). In this specific case of transfer learning, since the convolution layer is freezed, the weights are not being updated. Besides, the last fully connected layer is trained with random weight initialization based on the target dataset. So, in this scenario, what is the role of pre-trained weights from ImageNet? Or is it only about using the pre-defined renet-18 architecture? Kindly reply.