Recently I am working on the neural network deep learning algorithms, just curious to ask is it possible to merge two neural network models and to output one model that contains all the learned knowledge from the two models?
Model A can work on feature [A, B]
Model B can work on feature [C, D]
(Model type is the same for model A and model B if this makes things easier.)
After model merging, we get model C which is capable of working on feature [A, B, C, D].
I have looked into the transfer learning and Siamese network, but to my knowledge, I don't think these techniques can help me achieve my goal? (please correct me if I am wrong).
So any ideas will be much appreciated for merging the two models, or any possible technique, terminologies are welcomed.