I'm building a classification model that will need to classify into one of many possible outputs. I know in advance that I will need to add and subtract nodes from the output layer as circumstances change. Please refer me to any resource you are aware of that can help me understand potential approaches. I can retrain the model from scratch every time, of course. I'm looking for alternatives. I am only aware of two approaches:
- Transfer learning: Only retraining the final layer with the new desired outputs.
- Stacking one binary classifier for every potential output and streaming my data through all of them.
I need a level of specificity that is pretty granular, for example, I'd like to be able to identify a document that is primarily focused on a topic like "Changes in goat-herding practices in Uzbekistan." This is a made-up requirement, but the point is that it may have very specific features. Please provide input on the pros and cons of the approaches that I mentioned and add any approaches that I haven't thought of.