Greetings!
I’d like to ask for a suggestion from this venerable community on how I can handle a loss for a mulit-task network?
The data goes forward in parallel in an Unet-like network with two heads performing semantic segmentation with different targets (not a multi-class configuration, the tasks differ).
I was unable to find hints here and on Github.
Please suggest or forward where I can find examples.
Should I use two losses for each respective head and sum them?
But I wonder how exactly it is achieved, is it possible without writing a custom loss function?
Both heads use torch.nn.CrossEntropyLoss(), can I add them explicitly or should I write a custom implementation for the loss?
Thank you in advance,  I apologize if I violated any rules with my first question (I have an impression the topics are not categorized well here).