Although we use the weighted random sampler to alleviate the problem of the imbalanced dataset, repeating the very limited data instances (less than 3k) of a group during training cannot really help the posterior and prior networks to give a reasonable distribution that can generalize well on unseen inputs.
when the model comes into the instances in the testing dataset, the performance can be less satisfactory.