CLEAR: A New Framework for Long-Tailed Classification

A recent publication on arXiv (2608.11287) presents CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), an ensemble framework aimed at tackling long-tailed classification issues, where models exhibit varying reliability across prevalent and less common classes due to imbalanced training data. This approach creates a range of experts through structured sampling based on thresholds while maintaining the complete label space. It calculates a trust score for each expert using a smoothed precision formula for each class. During the inference phase, predictions from experts are merged using class-wise generalized product-of-experts aggregation, highlighting different experts for various classes. Experiments conducted on CIFAR datasets are mentioned, although the abstract is incomplete. Unlike existing methods that focus on re-balancing or representation learning, this approach specifically identifies which expert to trust for each class. This research is significant for machine learning and artificial intelligence, especially in scenarios involving class imbalance.