Looking at the plot, I’m noticing some anti correlation between predictions for classes below and above k~0.5.
It makes improving the global test accuracy difficult as high k labelled images only improve in test score when the low k labelled images drop in test scoring.
This is undoubtedly because of the imbalanced dataset but is there a way to stop this happening without collecting more data? As the frequency of occurrences of low k values images is very very low.
I wonder if anyone has any advice on dealing with regression problems where the diversity of images is very high in a class who’s frequency of occurrence is very low?