Is there an approach for the following problem:
Lets say, I trained a neural network on a big dataset for categorizing different fruits in $k$ classes. Afterwards I got a nice model, which performs very well.
Now I want to use the model for categorizing fruits in the corresponding $k$ classes, as it was planned beforehand. Unfortunately the fruits I want to categorize now are all not ripe yet, but my training set consisted only of ripe fruits. Furthermore I have some pictures of these not ripe fruits, but no labels.
How can I adapt my neural network to these slightly different domain with my pictures of not ripe fruits (and no labels!). Performance on the old task does not matter. The only thing I want, is categorizing not ripe fruits.
My only Idea now is to use virtual adversarial training (VAT) for the unlabeled pictures.