No problem! Funnily, this just came out and seems like the most comprehensive and promising of the bunch. Having skimmed through it, I believe certain basic mathematical functions (addition, pooling, etc.) are combined through AutoML to give a robust loss function. It is more generic and can be applied to object detection, segmentation, and more.
The results seem encouraging and it crushes traditional loss functions like cross entropy, dice loss, etc. in many cases, but is on par with other loss functions derived via grid search (unlike other methods though, it’s more generic).
Given its recency, I unfortunately wasn’t able to find an implementation. Might do it myself in the (near) future.
Cheers!