I am doing semantic segmentation task and I already train a model with a dataset that provides class label.
Now, I have a new dataset that only provide category label rather than the class labels I used to train. But, to utilize the pretrained model, I have to either adapt the prediction layer channels from number of classes to number of category; or, I map the predicted class labels to category labels, then use this to compute cross entropy loss as before.
For me, the second option sounds optimal, because all layers in the pretrained model can be applied.
But, if I modify the predicted label, what would be the consequence to autograd?
Thanks