【24h】

Image Clustering under Domain Shift

机译:域移位下的图像聚类

获取原文

摘要

We address domain adaptation in the context of clustering where we are given a set of unlabeled data, coming from several domains, and the goal is to group data into different categories regardless of the domain they come from. This is a challenging problem since we do not have any supervision unlike most adaptation scenarios studied earlier, and is very relevant in practical industry applications where labeled data often comes at a premium especially while deploying services that do not have a comparable predecessor. Our philosophy in addressing this problem draws motivation from the concept of dirty paper coding, a communications technique where the signal being transmitted through a noisy channel is encoded with priors on the possible noise patterns to assist reliable decoding of the signal at the receiver. We focus on image applications in this paper, where we encode priors on possible image domain shift factors such as viewpoint, lighting, blur and appearance variations and utilize geometric adaptation mechanisms to perform clustering. We illustrate the utility of our approach on standard datasets involving objects and faces, by obtaining around 18% improvement on average over existing approaches.
机译:我们在群集的背景下解决域适应问题,在群集中,我们获得了一组来自多个域的未标记数据,并且目标是将数据分为不同的类别,而不管它们来自哪个域。这是一个具有挑战性的问题,因为我们没有像先前研究的大多数适应方案那样进行任何监督,并且在实际工业应用中非常相关,在实际应用中,标记数据通常非常宝贵,尤其是在部署没有可比的前身的服务时。我们解决这一问题的理念是从脏纸编码的概念中汲取灵感的。脏纸编码是一种通信技术,其中通过有噪声信道传输的信号会先验编码在可能的噪声模式上,以帮助接收机进行可靠的信号解码。在本文中,我们专注于图像应用,在此我们对可能的图像域偏移因子(例如视点,照明,模糊和外观变化)进行先验编码,并利用几何适应机制执行聚类。通过比现有方法平均获得约18%的改善,我们说明了我们的方法在涉及对象和面部的标准数据集上的效用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号