Most applications of machine learning to classification assume a closed set of balanced classes.

However, existing frameworks face several interrelated limitations.

It appears that individuals experience different numbers of small and large growth spurts that differ in magnitude and timing.

Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.