Abstract
Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a deep network. For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data manifold in a small neighborhood of a point. These neighborhoods are used to estimate similarity between data points. We empirically show that this similarity estimate correlates better with the ground truth than the similarity estimates of current state-of-the-art techniques. We also show that proxies, commonly used in supervised metric learning, can be used to model the piecewise-linear manifold in an unsupervised setting, helping improve performance. Our method outperforms existing unsupervised metric learning approaches on standard zero-shot image retrieval benchmarks.
Abstract (translated)
无监督深度度量学习（UDML）关注使用未标记数据学习语义表示空间。这个具有挑战性的问题要求准确估计数据点之间的相似性，用于指导深度网络。为此，我们提出使用分块线性近似来建模高维数据流形，其中每个低维线性片段在点的一个小邻域内近似数据流形。这些邻域用于估计数据点之间的相似性。我们通过实验证明，这种相似性估计与地面真实值的相关性比现有技术的相似性估计更好。我们还证明了在无需标注的情况下，代理商（通常用于监督度量学习）可以用于在无监督环境中建模分块线性流形，从而提高性能。我们的方法在标准零散图像检索基准上优于现有的无监督度量学习方法。
URL
https://arxiv.org/abs/2403.14977
