CNUSVM: Hybrid CNN-uneven SVM model for imbalanced visual learning

Recently, deep Convolutional Neural Networks (CNNs) have been used to achieve state-of-the-art performance on a wide range of visual learning tasks. However, when facing some imbalanced learning tasks where the training samples are unevenly distributed among different classes, CNNs tend to produce performance bias toward the majority class, making them not suitable for applications in which the recognition ability on the minority class is highly valued. To address the problem, this paper proposes a hybrid classification model by combining CNN with Support Vector Machine (SVM) that has uneven margins. In this model, CNN works as a feature extractor and the extracted features are then sent into a L2-SVM with linear uneven margins. We also develop a gradient-descent learning approach for this hybrid CNN-uneven SVM (CNUSVM) model by minimizing an uneven margin based L2-hinge loss. Our experiments on two benchmark datasets show that the CNUSVM model can make more favorable decisions for imbalanced visual learning tasks in comparison with the standard CNN and the hybrid CNN-SVM model.