arXiv:2606.07630v1 Announce Type: new Abstract: Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly on minority classes. Among existing solutions, active learning offers an effective and efficient paradigm by selectively querying the most informative and balanced samples for annotation. We propose an innovative active learning framework that mitigates class imbalance and selects the most informative samples to annotate. Leveraging foundation model priors, our algorit
The rapid advancement and adoption of foundation models create new opportunities to address long-standing challenges in machine learning, such as data scarcity and class imbalance, becoming particularly relevant as AI systems move into real-world, data-intensive applications.
This development can significantly improve the efficiency and efficacy of AI training, making robust AI more accessible while reducing annotation costs, directly impacting the speed of AI deployment and the quality of resulting applications by overcoming data limitations.
The approach to data annotation and model training for imbalanced datasets changes, moving from labor-intensive manual curation to more automated, intelligent selection using powerful pre-trained models, thereby streamlining the AI development pipeline.
- · AI developers
- · Data annotation services
- · Industries with imbalanced datasets
- · Foundation model providers
- · Traditional, labor-intensive data labeling firms
- · AI models without active learning integration
AI models will perform better on minority classes in real-world applications due to improved data efficiency.
The cost and time required for developing high-performing AI systems will decrease, accelerating AI adoption across more sectors.
Enhanced AI capabilities derived from optimized data utilization could inadvertently exacerbate data privacy concerns as more sophisticated models become easier to train and deploy.
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