Abstract:
Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications including computer vision. The existing classification methods usually suffer from the limitation of data especially the 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 data perturbation and model perturbation. Specially, the proposed generative classifier is modeled by a deep latent variable model where the latent variable aims to capture the direct cause of target label. Meanwhile, the latent variable is represented by a probability distribution over possible values rather than a single fixed value, which is able to enforce uncertainty of model and lead to stable prediction. Furthermore, this latent variable, as a confounder, affects the process of data (feature/label) generation, so that we can arrive at well-justified sampling variability considerations in statistics, and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. By comparing with the state-of-the-art methods, experimental results demonstrate the superiority of our proposed model on imbalance classification task.
