Data mixing methods play a crucial role in semi-supervised learning (SSL),
but their application is unexplored in long-tailed semi-supervised learning
(LTSSL). The primary reason is that the in-batch mixing manner fails to address
class imbalance. Furthermore, existing LTSSL methods mainly focus on
re-balancing data quantity but ignore class-wise uncertainty, which is also
vital for class balance. For instance, some classes with sufficient samples
might still exhibit high uncertainty due to indistinguishable features. To this
end, this paper introduces the Balanced and Entropy-based Mix (BEM), a
pioneering mixing approach to re-balance the class distribution of both data
quantity and uncertainty. Specifically, we first propose a class balanced mix
bank to store data of each class for mixing. This bank samples data based on
the estimated quantity distribution, thus re-balancing data quantity. Then, we
present an entropy-based learning approach to re-balance class-wise
uncertainty, including entropy-based sampling strategy, entropy-based selection
module, and entropy-based class balanced loss. Our BEM first leverages data
mixing for improving LTSSL, and it can also serve as a complement to the
existing re-balancing methods. Experimental results show that BEM significantly
enhances various LTSSL frameworks and achieves state-of-the-art performances
across multiple benchmarks.

本文介绍了一种平衡和基于熵的混合（BEM）方法，以重新平衡长尾半监督学习中的类分布，并通过数据混合改善长尾半监督学习，实验证明 BEM 显著提高了各种长尾半监督学习框架，在多个基准测试中达到了最先进的性能。