I’m confused about the backpropagation in end-to-end experiments. I replace the layer fc of Resnet50 with a CNN classifier. The input of CNN is a feature-map with size 100x2048, each row of the feature-map is generated by Resnet50 and they are just simply concatenated to form a feature-map, which means that several outputs of Resnet50 form one input of CNN classifier. So is it possible to backpropagate gradients from CNN classifier to Resnet50 when the loss function is based on the prediction of CNN classifier?