wally88:

do you think I should treat this problem as 5 different multiclass classifiers and in this case is there a way to write the network so I don’t have to run the same network 5 times


You could create 5 different “heads” in your model, which just uses a small classifier (e.g. single linear layer) to predict the different classes.
This approach would reuse the complete model and just run all heads separately.



 wally88:

or more shall I encode each of the 5 classes as one hot and go for the standard classification postprocessing the output of the network splitting it accordingly to the 5 classes and then do argmax, or even using the binary_crossentropy loss?


I’m not sure I understand this approach. Could you explain it a bit more?



 wally88:

or thinking of the problem as multi label classification?


This might work, although the constraint that only 5 classes can be active at all times will be lost.
Still a valid approach and worth a try in my opinion.