WI classifier (CNN) training about sigver_wiwd HOT 2 CLOSED

In this paper we evaluated different formulations of the problem, considering scenarios where only genuine signatures are available, and scenarios where both genuine signatures and forgeries are used, see section 3 of the paper:

3.1 Learning feature from genuine signatures: only genuine signatures were used. In the paper, we refer to this as "SigNet"
3.2.1 Treat forgeries as separate classes: in this formulation, forgeries for different users have each a different label, so you have twice as many classes as the number of users.
3.2.2 Add a separate output for detecting forgeries: in this formulation, we consider two objective functions: learning to discriminate the user, and learning to separate genuine signatures and forgeries. That is, the dataset is now of the format (X, y, f), where X is the signature, y is the class (user) and f is a binary variable that indicates if the signature is a forgery. The forgeries have the same class (y) as the genuine. Note, however, that the loss function that worked best ("L2", equation (4) in the paper) does not consider the label y of the forgeries (that is, genuine signatures contribute to both losses, while forgeries only contribute to the "forgery classification loss"). In the paper, we refer to this model as "SigNet-F"

I believe this answers the first question. For the second question ("Were all forgeries for all users given same label?"): we have not tried this alternative in this paper

from sigver_wiwd.

ok.

from sigver_wiwd.