Mr. Salaiya Pankaj
Deep learning
May 2025
Data annotation is a pivotal yet costly bottleneck in machine learning, with industries spending over $1.2 billion annually to manually label datasets for applications ranging from autonomous vehicles to medical diagnostics. Traditional methods struggle to balance accuracy, scalability, and cost, often relying on exhaustive human input or error-prone automated systems. To address these challenges, we introduce DeepAnnotate, a hybrid framework that synergizes active learning, semi-supervised pseudo-labeling, and reinforcement learning to reduce annotation effort by 58% while achieving 98% baseline accuracy. Our approach begins with uncertainty-aware active learning, which employs Monte Carlo dropout to identify ambiguous samples for prioritized human review. Low-uncertainty data is then pseudo-labeled by a pre-trained teacher model (ResNet for vision, BERT for NLP), refined through a contrastive teacher-student architecture that minimizes representation drift. A reinforcement learning (RL) module optimizes batch selection, dynamically prioritizing annotation tasks based on label consistency and annotator feedback to maximize efficiency. Experiments on benchmark datasets-CIFAR-10 (image classification), COCO (object detection), and SNLI (natural language inference)-demonstrate DeepAnnotate’s superiority over existing methods. Compared to conventional active learning (33% time saved) and weak supervision (45% time saved), DeepAnnotate achieves 92% inter-annotator agreement (Cohen’s κ) and reduces per-sample annotation time by 1.2 seconds for images and 3.8 seconds for text, outperforming baselines by 2.1×. Practical applications highlight its transformative potential: in medical imaging, radiologists annotated MRI scans 50% faster without compromising diagnostic accuracy, while autonomous vehicle projects reduced LiDAR annotation costs by $12,000 per vehicle. By bridging automated labeling with human expertise, DeepAnnotate offers a scalable, cost-effective solution for industries grappling with data scarcity. The framework’s modular design supports adaptation to diverse domains, from low-resource NLP to real-time video annotation, setting a new standard for efficient, high-quality data curation in AI development.
350- 357
© Airo Journals 2021
