Towards Effective Collaborative Learning in Long-Tailed Recognition
Zhengzhuo Xu Zenghao Chai Chengyin Xu Chun Yuan Haiqin Yang
Real-world data usually suffers from severe class imbalance and long-tailed
distributions, where minority classes are significantly underrepresented
compared to the majority ones. Recent research prefers to utilize multi-expert
architectures to mitigate the model uncertainty on the minority, where
collaborative learning is employed to aggregate the knowledge of experts, i.e.,
online distillation. In this paper, we observe that the knowledge transfer
between experts is imbalanced in terms of class distribution, which results in
limited performance improvement of the minority classes. To address it, we
propose a re-weighted distillation loss by comparing two classifiers'
predictions, which are supervised by online distillation and label annotations,
respectively. We also emphasize that feature-level distillation will
significantly improve model performance and increase feature robustness.
Finally, we propose an Effective Collaborative Learning (ECL) framework that
integrates a contrastive proxy task branch to further improve feature quality.
Quantitative and qualitative experiments on four standard datasets demonstrate
that ECL achieves state-of-the-art performance and the detailed ablation
studies manifest the effectiveness of each component in ECL.