Learning from Reduced Labels for Long-Tailed Data
Meng Wei Zhongnian Li Yong Zhou Xinzheng Xu
Long-tailed data is prevalent in real-world classification tasks and heavily
relies on supervised information, which makes the annotation process
exceptionally labor-intensive and time-consuming. Unfortunately, despite being
a common approach to mitigate labeling costs, existing weakly supervised
learning methods struggle to adequately preserve supervised information for
tail samples, resulting in a decline in accuracy for the tail classes. To
alleviate this problem, we introduce a novel weakly supervised labeling setting
called Reduced Label. The proposed labeling setting not only avoids the decline
of supervised information for the tail samples, but also decreases the labeling
costs associated with long-tailed data. Additionally, we propose an
straightforward and highly efficient unbiased framework with strong theoretical
guarantees to learn from these Reduced Labels. Extensive experiments conducted
on benchmark datasets including ImageNet validate the effectiveness of our
approach, surpassing the performance of state-of-the-art weakly supervised
methods.