We shall assume that we have a trained neural network model for some task $A$. The dataset used to train the model contained some $n$ features per sample. Using this dataset, we were able to train a classifier.
After some time, I get some more data for the same task $A$, but with additional features (all relevant). I want to use this new data to improve my existing classifier by utilizing the weights. (whatever it learned from the initial dataset).
This seems very similar to the problem of transfer learning, but my aim is to use an existing model architecture without modifying it too much and make it a better classifier for the same task A without having to do any retraining. (We can assume that data is lost after training, and all we have are the parameters/weights of our model)
After doing some reading, this could be a use case of continual learning but most of the work is for training an existing model for different tasks. I am not able to fully wrap my head around how I could use the same model for the same task, but instead, the structure of the data is changing over time. Can you also refer me to any literature in this space, if any?