Thushar Tom
Papers
1
Total Citations
1
H-Index
1
About
Thushar Tom is a researcher at the forefront of applied deep learning, with a focus on domain adaptation, computer vision, and explainable AI (XAI) for industrial automation. His work addresses a critical bottleneck in manufacturing: the scarcity of annotated real-world data for training high-accuracy models. In his most cited paper, "Domain Adaptation Using Vision Transformers and XAI for Fully Synthetic Industrial Training" (2025), Tom introduces a novel framework that leverages Vision Transformers and XAI techniques to enable models trained entirely on synthetic data to perform effectively in real industrial settings—such as robot-based material handling and quality inspection. This approach not only reduces the need for costly manual annotation but also enhances model transparency and trustworthiness. With 1 citation to date, this work is already gaining traction in the robotics and manufacturing communities. Tom’s contributions are pivotal for advancing scalable, cost-efficient AI solutions in Industry 4.0, and his research promises to bridge the gap between simulation and real-world deployment, making him a key figure in the future of smart manufacturing.
Research Focus
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Top Papers
- 1