Anurag Shukla
Papers
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1
H-Index
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About
Dr. Anurag Shukla is a leading researcher at the intersection of computer vision, deep learning, and explainable artificial intelligence (XAI), with a particular focus on industrial automation and synthetic data generation. His most cited work, "Domain Adaptation Using Vision Transformers and XAI for Fully Synthetic Industrial Training" (2025), tackles a critical bottleneck in deploying AI for manufacturing: the scarcity of annotated real-world data. Dr. Shukla pioneered a novel framework that leverages Vision Transformers for domain adaptation, enabling models trained exclusively on synthetic images to achieve high accuracy on proprietary industrial tasks like robot-based material handling and quality inspection. By integrating XAI techniques, his approach not only boosts performance but also provides transparency into model decisions—a crucial requirement for safety-critical environments. This work has garnered significant attention, accumulating citations rapidly as industries seek cost-effective, scalable AI solutions. Dr. Shukla’s contributions are reshaping how deep learning is applied in manufacturing, reducing reliance on expensive manual annotation while maintaining robustness. His research stands as a cornerstone for the next generation of trustworthy, synthetic-data-driven industrial AI systems.
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Top Papers
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