@iScienceLuvr
That's where the FINO framework comes in. The main innovation here is the incorporation of guidance with metadata you already have to better adapt the learned representations. The authors utilize two types of metadata to guide training: informative factors that should shape the representation (an antibody label in microscopy, geography in satellite imagery) are encouraged, while spurious factors that just reflect how the data was collected (the imaging plate, the sensor resolution) are actively suppressed via gradient reversal.