  The base learners and labeled samples (shots) in an ensemble few-shot
classifier greatly affect the model performance. When the performance is not
satisfactory, it is usually difficult to understand the underlying causes and
make improvements. To tackle this issue, we propose a visual analysis method,
FSLDiagnotor. Given a set of base learners and a collection of samples with a
few shots, we consider two problems: 1) finding a subset of base learners that
well predict the sample collections; and 2) replacing the low-quality shots
with more representative ones to adequately represent the sample collections.
We formulate both problems as sparse subset selection and develop two selection
algorithms to recommend appropriate learners and shots, respectively. A matrix
visualization and a scatterplot are combined to explain the recommended
learners and shots in context and facilitate users in adjusting them. Based on
the adjustment, the algorithm updates the recommendation results for another
round of improvement. Two case studies are conducted to demonstrate that
FSLDiagnotor helps build a few-shot classifier efficiently and increases the
accuracy by 12% and 21%, respectively.
