I’m working on data sets with animals of the same species, but recorded in different locations and with varying camera angles, zoom levels, light conditions, and so on. Moving forward we will be recording in more new locations and again with varying conditions.
I am therefore in the process of testing how well a network trained in one location performs in a new location.
My question is this:
When using a network that I’ve already trained as a base (initial weight), and adding new data (from new location), should I then A) keep the data from the first location in the training data set, or B) train on the data from the new location only?
My concern is that A might lead to overfitting since the network will end up having trained for a very large number of iterations on the base data set or that B might lead to the network, sort of, drifting and “forgetting” what it learned while training on the base data set.
I hope my question makes sense.