# How to optimize neural network with mutile losses

There is a multi-task problem that I try to solve with a single neutral network. In general it works fine, but it seems like there is a room for improvement.

The final loss to optimize looks like this

$$loss = (task_1loss + task_2loss + task_3loss)/3$$

Of course, all losses are on different scales, and as result, the overall loss is dominated by the highest loss. I wonder if there is best practices how to normalize tasks losses or parametrize them.

Thank you.