Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications. Existing classification methods usually suffer from the limitation of data especially for minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation. Specially, the proposed generative classifier is derived from a deep latent variable model where two variables are involved. One variable is to capture the essential information of the original data, denoted as latent codes, which are represented by a probability distribution rather than a single fixed value. The learnt distribution aims to enforce the uncertainty of model and implement model perturbation, thus, lead to stable predictions. The other variable is a prior to latent codes so that the codes are restricted to lie on components in Gaussian Mixture Model. As a confounder affecting generative processes of data (feature/label), the latent variables are supposed to capture the discriminative latent distribution and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. Experimental results demonstrate the superiority of our proposed model by comparing with popular imbalanced classification baselines on imbalance classification task.

中文翻译：

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用于鲁棒不平衡分类的深度生成混合模型


从不平衡数据中发现隐藏模式是各种实际应用中的关键问题。现有的分类方法通常受到数据的限制，特别是对于少数类别，导致预测不稳定和性能低下。在本文中，提出了一种深度生成分类器，通过模型扰动和数据扰动来缓解这个问题。特别地，所提出的生成分类器是从涉及两个变量的深层潜变量模型导出的。一个变量是捕获原始数据的基本信息，表示为潜在代码，它由概率分布而不是单个固定值表示。学习的分布旨在增强模型的不确定性并实现模型扰动，从而获得稳定的预测。另一个变量是潜在代码的先验，因此代码仅限于高斯混合模型中的组件。作为影响数据（特征/标签）生成过程的混杂因素，潜在变量应该捕获有区别的潜在分布并实现数据扰动。在广泛使用的真实不平衡图像数据集上进行了大量的实验。通过与不平衡分类任务上流行的不平衡分类基线进行比较，实验结果证明了我们提出的模型的优越性。