Convolutional Ensembling based Few-Shot Defect Detection Technique
- URL: http://arxiv.org/abs/2208.03288v1
- Date: Fri, 5 Aug 2022 17:29:14 GMT
- Title: Convolutional Ensembling based Few-Shot Defect Detection Technique
- Authors: Soumyajit Karmakar, Abeer Banerjee, Sanjay Singh
- Abstract summary: We present a new approach to few-shot classification, where we employ the knowledge-base of multiple pre-trained convolutional models.
Our framework uses a novel ensembling technique for boosting the accuracy while drastically decreasing the total parameter count.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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