论文标题
GUCNET：基于指导聚类的网络，用于改进分类
GuCNet: A Guided Clustering-based Network for Improved Classification
论文作者
论文摘要
我们处理了具有挑战性和混乱的数据集的语义分类问题。我们通过利用任何现有良好可分离数据集的可分离性来指导，提出了一种非常简单的分类技术。由于指南数据集可能与实验数据集有或可能没有任何语义关系，形成了功能集中良好的可分离群集，因此拟议的网络试图将挑战性数据集的班级特征嵌入到指南集的那些独特的群集中，从而使它们更可分开。根据可用性，我们提出了两种类型的指南集：一种使用纹理（图像）指南，另一种使用代表集群中心的原型向量。在具有挑战性的基准RSSCN，LSUN和TU-BERLIN数据集上获得的实验结果确定了所提出方法的功效，因为我们以相当大的余量优于现有的最新技术。
We deal with the problem of semantic classification of challenging and highly-cluttered dataset. We present a novel, and yet a very simple classification technique by leveraging the ease of classifiability of any existing well separable dataset for guidance. Since the guide dataset which may or may not have any semantic relationship with the experimental dataset, forms well separable clusters in the feature set, the proposed network tries to embed class-wise features of the challenging dataset to those distinct clusters of the guide set, making them more separable. Depending on the availability, we propose two types of guide sets: one using texture (image) guides and another using prototype vectors representing cluster centers. Experimental results obtained on the challenging benchmark RSSCN, LSUN, and TU-Berlin datasets establish the efficacy of the proposed method as we outperform the existing state-of-the-art techniques by a considerable margin.
