arXiv:2606.08718v1 Announce Type: new Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors. This is because the data sampled for active learning is assumed to be highly informative for training. When human annotators introduce errors into this informative data at a certain rate, the active learning performance drops significantly and, in some cases, even exhibits worse outcomes than passive learning. In this paper, we first analyze the impact of human annotation errors in the DAL setting. Then we propose a
The increasing scale and complexity of AI models necessitate more efficient and reliable data annotation, making robust active learning critical.
Improving annotation efficiency and noise resilience directly impacts the cost and performance of AI model development, a key bottleneck for many applications.
The proposed 'Deep Active Re-Labeling' method offers a pathway to more cost-effective and accurate AI training, potentially accelerating AI adoption and performance in real-world scenarios.
- · AI model developers
- · Data annotation services
- · Sectors reliant on accurate AI
- · Researchers in active learning
- · Inefficient manual annotation methods
Reduced cost and time for AI model development due to more efficient data labeling processes.
Faster iteration cycles and deployment of AI systems across various industries.
Increased overall efficiency and reliability of AI applications, leading to broader societal integration of AI.
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