How to train on extended data set correctly I have trained my classifier on pictures with a mixture of several classes
on each picture, e.g. A-F. The classifier is able to (nearly) correctly segment those classes
on the images.

Now I got more data with pictures showing class G. To minimize my work, I only labeled
class G on the images and left the rest out (Invalid). 

Two questions for my training arise:  


If there are no examples of class G in my first dataset (because it could have been a subclass
of A-F), how do I train it correctly? 
Suggested Solution: I can add my new data to the old one, but I have to tell the CNN when there is an image with class G, because I have to change the error measurement of my CNN somehow. But how?  
If G was included in my first dataset (assume G is 'police car' and B is 'car', but
some images showed police car), how do I train in this case correctly?
Transfer learning is what you want to need.

Checkout this note from CS231n, it provides some general advices on transfer learning and model fine-tuning.

In the meanwhile, this blog from keras shows how to use pre-trained VGG16 network to classify dogs&amp;cats.