Convolutional Ensembling based Few-Shot Defect Detection Technique. (arXiv:2208.03288v3 [cs.CV] UPDATED)

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 …

arxiv defect detection detection