Sorry if this more of a general methodology question than PyTorch.
I’m training a mushroom classifier, and some classes might be merged for various reasons (classes are small, labels are often switched, the end user will consider them similar, etc). Is it best to create the merged classes before training or train with the original classes and “merge” afterwards?
If I merge afterwards , should I add up the probability estimates after soft-maxing or combine (add) the neural net output before applying softmax?
If merging afterwards usually works equally well, it has the advantage that one can experiment with different mergers without retraining.