We propose a new strategy to improve the accuracy and robustness of image classification. First, we train a baseline CNN model. Then, we identify challenging regions in the feature space by identifying all misclassified samples, and correctly classified samples with low confidence values. These samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE is used to generate synthetic images. Finally, the generated synthetic images are used in conjunction with the original labeled images to train a new model in a semi-supervised fashion. Empirical results on benchmark datasets such as STL10 and CIFAR-100 show that the synthetically generated samples can further diversify the training data, leading to improvement in image classification in comparison with the fully supervised baseline approaches using only the available data.

翻译：我们提出了提高图像分类准确性和稳健性的新战略。 首先,我们培训了一个有线电视新闻网基线模型。 然后,我们通过辨别所有分类不当的样本和准确保密的低信任值样本,确定地貌空间中具有挑战性的区域。然后,这些样本被用于培训一个变化式自动编码器(VAE),然后,利用VAE生成合成图像。最后,生成的合成图像与原贴标签图像一起,以半监督的方式培训一个新的模型。 STL10和CIFAR-100等基准数据集的经验性结果显示,合成生成的样本可以进一步使培训数据多样化,导致与仅使用现有数据的全面监督基线方法相比,图像分类的改进。