  Over the past few years, there has been a significant improvement in the
domain of few-shot learning. This learning paradigm has shown promising results
for the challenging problem of anomaly detection, where the general task is to
deal with heavy class imbalance. Our paper presents a new approach to few-shot
classification, where we employ the knowledge-base of multiple pre-trained
convolutional models that act as the backbone for our proposed few-shot
framework. Our framework uses a novel ensembling technique for boosting the
accuracy while drastically decreasing the total parameter count, thus paving
the way for real-time implementation. We perform an extensive hyperparameter
search using a power-line defect detection dataset and obtain an accuracy of
92.30% for the 5-way 5-shot task. Without further tuning, we evaluate our model
on competing standards with the existing state-of-the-art methods and
outperform them.
