PMAL: A Proxy Model Active Learning Approach for Vision Based Industrial Applications

Author:

Khan Abbas1ORCID,Haq Ijaz Ul1ORCID,Hussain Tanveer1ORCID,Muhammad Khan2ORCID,Hijji Mohammad3ORCID,Sajjad Muhammad4ORCID,De Albuquerque Victor Hugo C.5ORCID,Baik Sung Wook1ORCID

Affiliation:

1. Sejong University, Seoul, Republic of Korea

2. Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea

3. Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk, Saudi Arabia

4. Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar, Pakistan

5. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Fortaleza/CE, Brazil

Abstract

Deep Learning models’ performance strongly correlate with availability of annotated data; however, massive data labeling is laborious, expensive, and error-prone when performed by human experts. Active Learning (AL) effectively handles this challenge by selecting the uncertain samples from unlabeled data collection, but the existing AL approaches involve repetitive human feedback for labeling uncertain samples, thus rendering these techniques infeasible to be deployed in industry related real-world applications. In the proposed Proxy Model based Active Learning technique (PMAL) , this issue is addressed by replacing human oracle with a deep learning model, where human expertise is reduced to label only two small subsets of data for training proxy model and initializing the AL loop. In the PMAL technique, firstly, proxy model is trained with a small subset of labeled data, which subsequently acts as an oracle for annotating uncertain samples. Secondly, active model's training, uncertain samples extraction via uncertainty sampling, and annotation through proxy model is carried out until predefined iterations to achieve higher accuracy and labeled data. Finally, the active model is evaluated using testing data to verify the effectiveness of our technique for practical applications. The correct annotations by the proxy model are ensured by employing the potentials of explainable artificial intelligence. Similarly, emerging vision transformer is used as an active model to achieve maximum accuracy. Experimental results reveal that the proposed method outperforms the state-of-the-art in terms of minimum labeled data usage and improves the accuracy with 2.2%, 2.6%, and 1.35% on Caltech-101, Caltech-256, and CIFAR-10 datasets, respectively. Since the proposed technique offers a highly reasonable solution to exploit huge multimedia data, it can be widely used in different evolutionary industrial domains.

Funder

Institute of Information & Communications Technology Planning & Evaluation

Korea government

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Reference54 articles.

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