CvS: Classification via Segmentation For Small Datasets
- URL: http://arxiv.org/abs/2111.00042v1
- Date: Fri, 29 Oct 2021 18:41:15 GMT
- Title: CvS: Classification via Segmentation For Small Datasets
- Authors: Nooshin Mojab, Philip S. Yu, Joelle A. Hallak, Darvin Yi
- Abstract summary: This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps.
We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.
- Score: 52.821178654631254
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning models have shown promising results in a wide range of computer
vision applications across various domains. The success of deep learning
methods relies heavily on the availability of a large amount of data. Deep
neural networks are prone to overfitting when data is scarce. This problem
becomes even more severe for neural network with classification head with
access to only a few data points. However, acquiring large-scale datasets is
very challenging, laborious, or even infeasible in some domains. Hence,
developing classifiers that are able to perform well in small data regimes is
crucial for applications with limited data. This paper presents CvS, a
cost-effective classifier for small datasets that derives the classification
labels from predicting the segmentation maps. We employ the label propagation
method to achieve a fully segmented dataset with only a handful of manually
segmented data. We evaluate the effectiveness of our framework on diverse
problems showing that CvS is able to achieve much higher classification results
compared to previous methods when given only a handful of examples.
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