I’m working on a supervised deep learning model. The loss used should be different depends on the class of the input.
For example : If X_0 is the input vector and its from the class 0, resp X_1 from the class 1. The used loss for X_0 will be L_0 and the one for X_1 will be L_1.
Now we are in a training session. Inside the current batch we have D our current data of n samples and Y their associated label (either 0 or 1).
What is the difference, if there is one, between :
Filtering the calcul though index selection
LOSS = L_0(D[class == 0]) + L_1(D[class == 1])
Forcing 0 for the unwanted calcul.
LOSS = (1-Y)L_0(D) + YL_1(D)