Earticle

초록

영어

Deep learning models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, a novel AL-based framework is proposed comprised of proxy and active models to reduce the manual labeling costs. In the proxy model, VGG-16 is trained on chunks of labeled data that later act as an annotator decision. On the other hand, in the active model, unlabeled is passed to Inception-V3 using the sampling strategy. The uncorrected predicted samples are then forwarded to the proxy model for annotation and considered those data have a high confidence score. The empirical results verify that our proposed model is the best in terms of annotation and accuracy.

목차

Abstract
1. Introduction
2. The proposed system
3. Experimental results
3.1. Dataset
3.2. Comparative analysis
5. Conclusions
Acknowledgment
References

저자

• Hikmat Yar [ Sejong University Seoul, Republic of Korea ]
• Samee Ullah Khan [ Sejong University Seoul, Republic of Korea ]
• Tanveer Hussain [ Sejong University Seoul, Republic of Korea ]
• Min Je Kim [ Sejong University Seoul, Republic of Korea ]
• Mi Young Lee [ Sejong University Seoul, Republic of Korea ]
• Sung Wook Baik [ Sejong University Seoul, Republic of Korea ] Correspondence author

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