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.


1 Answer 1


I think those are one of the most cited papers:

  • $\begingroup$ Thank you very much. There seem to be some very promising ideas for my problem. Take my upvote ;) $\endgroup$ Dec 18, 2018 at 16:45
  • $\begingroup$ You're welcome! Yes, these papers looks really exciting... I'll try this techniques soon, we could exchange our impressions and considerations :) $\endgroup$
    – ignatius
    Dec 18, 2018 at 16:46
  • $\begingroup$ I will definitely report back^^ $\endgroup$ Dec 18, 2018 at 16:46

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