  In multi-stage processes, decisions happen in an ordered sequence of stages.
Many of them have the structure of dual funnel problem: as the sample size
decreases from one stage to the other, the information increases. A related
example is a selection process, where applicants apply for a position, prize,
or grant. In each stage, more applicants are evaluated and filtered out, and
from the remaining ones, more information is collected. In the last stage,
decision-makers use all available information to make their final decision. To
train a classifier for each stage becomes impracticable as they can underfit
due to the low dimensionality in early stages or overfit due to the small
sample size in the latter stages. In this work, we proposed a
\textit{Multi-StaGe Transfer Learning} (MSGTL) approach that uses knowledge
from simple classifiers trained in early stages to improve the performance of
classifiers in the latter stages. By transferring weights from simpler neural
networks trained in larger datasets, we able to fine-tune more complex neural
networks in the latter stages without overfitting due to the small sample size.
We show that it is possible to control the trade-off between conserving
knowledge and fine-tuning using a simple probabilistic map. Experiments using
real-world data demonstrate the efficacy of our approach as it outperforms
other state-of-the-art methods for transfer learning and regularization.
