基于深度聚类的无监督特征嵌入学习

Unsupervised feature embedding learning via deep clustering

为了提高无监督嵌入学习对图像特征的判别能力,提出一种基于深度聚类的无监督学习方法。通过对图像的嵌入特征进行聚类,获得图像之间的伪类别信息,然后最小化聚类损失来优化网络模型,使得模型能够学习到图像的高判别性特征。在三个标准数据集上的图像检索性能表明了该方法的有效性,并且优于目前大多数方法。

In order to improve the ability of unsupervised embedding learning to distinguish image features,an unsupervised method based on deep clustering is proposed.By clustering the embedded features of images,the pseudo category information between images is obtained,and then the clustering loss is minimized to optimize the network model,so that the model can learn the high discriminant features of images.The performance of image retrieval on three standard data sets shows that the proposed method is effective and better than most of the current methods.