基于改进变分自编码器的零样本图像分类

Zero-Shot Image Classification Based on Improved Variational Auto-encoder

针对零样本图像分类过程中对于已知类别样本获得代价高、领域漂移等问题,提出了一种利用最大均值差异改进变分自编码器的零样本图像分类模型,首先用最大化均值差异分离样本噪声因素来得到更贴近未知类别的样本,接着利用生成的样本辅助学习将零样本分类问题转化为有监督学习分类问题,之后利用分类模型进行图像分类。相较于基于距离度量进行零样本图像分类的算法,提出的算法在CUB,AWA和ImageNet-2数据集上均得到良好的分类效果,改善领域漂移问题,分类精度得到提高,证明了算法模型的有效性和可行性。

In the process of zero-shot image classification,problems such as high acquisition cost for samples of known categories and domain drift were addressed.A zero-shot image classification model based on maximum mean difference was proposed to improve the variational auto-encoder.First,the noise factor of samples is separated by maximizing the mean difference to obtain samples closer to the unknown category.Then,the generated sample-assisted learning is used to transform the zero-shot classification problem into the supervised learning classification problem.Finally,the classification model is used for image classification.Compared with the zero-shot image classification algorithm based on distance measurement,the proposed algorithm achieved good classification effect on CUB,AWA,and ImageNet-2 data sets,and improved domain drift and classification accuracy,which proves the effectiveness and feasibility of the proposed algorithm model.