Update README.md

README.md

@@ -36,9 +36,13 @@ We provide four splits:
 - `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` test and the nominal mnist test set by LeCun et. al.,
 - `train_mixed`: 70'000 images, consisting
 
+Note that the ambiguous train images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), 
+the training set images allow for more unbalanced ambiguity. 
+This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
+
 For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`. 
 Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty. 
-Note that in related literature, these 'mixed' splits are sometimes denoted as *dirty* splits.
+In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
 
 ### Assessment and Validity
 For a brief discussion of the strength and weaknesses of this dataset,