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.
翻译:无监督深度度量学习(UDML)致力于仅使用无标注数据学习语义表示空间。这一具有挑战性的问题要求准确估计数据点之间的相似性,并将其用于深度网络的监督训练。为此,我们提出采用分段线性近似对高维数据流形进行建模,其中每个低维线性片段近似于某点邻域内的数据流形。这些邻域被用于估计数据点间的相似性。实验表明,与现有最优技术的相似性估计相比,我们的相似性估计与真实标签的相关性更高。我们还证明,监督度量学习中常用的代理变量可在无监督场景下有效建模分段线性流形,从而进一步提升性能。我们的方法在标准零样本图像检索基准上优于现有无监督度量学习方法。