Significant progress has been made in learning image classification neural networks under long-tail data distribution using robust training algorithms such as data re-sampling, re-weighting, and margin adjustment. Those methods, however, ignore the impact of data imbalance on feature normalization. The dominance of majority classes (head classes) in estimating statistics and affine parameters causes internal covariate shifts within less-frequent categories to be overlooked. To alleviate this challenge, we propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. In addition, a moving average-based expectation maximization (EM) algorithm is employed to estimate the statistical parameters of multiple Gaussian distributions. However, the EM algorithm is sensitive to initialization and can easily become stuck in local minima where the multiple Gaussian components continue to focus on majority classes. To tackle this issue, we developed a dual-path learning framework that employs class-aware split feature normalization to diversify the estimated Gaussian distributions, allowing the Gaussian components to fit with training samples of less-frequent classes more comprehensively. Extensive experiments on commonly used datasets demonstrated that the proposed method outperforms existing methods on long-tailed image classification.
翻译:在利用数据重新抽样、重新加权和边距调整等强有力的培训算法,在长尾数据分配下,在学习图像分类神经网络方面取得了显著进展。但是,这些方法忽略了数据不平衡对特征正常化的影响。多数类(头类)在估计统计数据和碳蜡参数方面的主导地位,导致在较不经常的类别中内部的共变变变化将被忽视。为了缓解这一挑战,我们提议了一个基于高斯混合物的复合批次正常化方法。它可以更全面地模拟特征空间并减少头类的主导地位。此外,还采用了移动平均预期最大化(EM)算法来估计多种高斯分布的统计参数。然而,EM算法对初始化十分敏感,很容易被困在本地迷你马中,因为多个高斯组的成分继续集中在多数类。为了解决这个问题,我们制定了一个双向学习框架,采用等级分立特性的分解特性使估计的高斯分布多样化,使高斯的组件能够与现有较不经常使用的图像分类方法的培训样本匹配。