related to How to Create CaffeDB training data for siamese networks out of image directory
If I have N labels. How can I enforce, that the feature vector of size N right before the contrastive loss layer represents some kind of probability for each class? Or comes that automatically with the siamese net design?
If you only use contrastive loss in a Siamese network, there is no way of forcing the net to classify into the correct label - because the net is only trained using "same/not same" information and does not know the semantics of the different classes.
What you can do is train with multiple loss layers.
You should aim at training a feature representation that is reach enough for your domain, so that looking at the trained feature vector of some input (in some high dimension) you should be able to easily classify that input to the correct class. Moreover, given that feature representation of two inputs one should be able to easily say if they are "same" or "not same".
Therefore, I recommend that you train your deep network with two loss layer with "bottom" as the output of one of the "InnerProduct" layers. One loss is the contrastive loss. The other loss should have another "InnerProduct" layer with num_output: N and a "SoftmaxWithLoss" layer.
A similar concept was used in this work: Sun, Chen, Wang and Tang Deep Learning Face Representation by Joint Identification-Verification NIPS 2014.