Adapting Neural Network to new domain without labels 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.
I think those are one of the most cited papers:


https://arxiv.org/pdf/1409.7495.pdf
http://www.jmlr.org/papers/volume17/15-239/15-239.pdf
http://openaccess.thecvf.com/content_cvpr_2017/paper/Tzeng_Adversarial_Discriminative_Domain_CVPR_2017_paper.pdf
http://openaccess.thecvf.com/content_cvpr_2017/papers/Bousmalis_Unsupervised_Pixel-Level_Domain_CVPR_2017_paper.pdf