I have a general question.
When starting a new project on either classification/localization, we often start with a pre-trained network. and fine-tune it for our special case.
Now, say the pre-trained network can handle already n classes and you fine-tune it to handle m custom classes. Is it possible to find an architecture such that we can actually now have a model that handle n + m classes ? Without applying model 1 and model 2 separately ?
Concretely, can we “enrich” a pre-trained network with new classes without having to train again on the full initial dataset ? Hope I’m being clear I’d guess we can freeze an output layer with the say 1000 classes from VGG and then on another branch fine-tune… Not sure tho
Thanks for your help !