Dimensionality reduction methods are unsupervised approaches which learn low-dimensional spaces where some properties of the initial space, typically the notion of "neighborhood", are preserved. They are a crucial component of diverse tasks like visualization, compression, indexing, and retrieval. Aiming for a totally different goal, self-supervised visual representation learning has been shown to produce transferable representation functions by learning models that encode invariance to artificially created distortions, e.g. a set of hand-crafted image transformations. Unlike manifold learning methods that usually require propagation on large k-NN graphs or complicated optimization solvers, self-supervised learning approaches rely on simpler and more scalable frameworks for learning. In this paper, we unify these two families of approaches from the angle of manifold learning and propose TLDR, a dimensionality reduction method for generic input spaces that is porting the simple self-supervised learning framework of Barlow Twins to a setting where it is hard or impossible to define an appropriate set of distortions by hand. We propose to use nearest neighbors to build pairs from a training set and a redundancy reduction loss borrowed from the self-supervised literature to learn an encoder that produces representations invariant across such pairs. TLDR is a method that is simple, easy to implement and train, and of broad applicability; it consists of an offline nearest neighbor computation step that can be highly approximated, and a straightforward learning process that does not require mining negative samples to contrast, eigendecompositions, or cumbersome optimization solvers. By replacing PCA with TLDR, we are able to increase the performance of GeM-AP by 4% mAP for 128 dimensions, and to retain its performance with 16x fewer dimensions.

翻译：降低尺寸的方法是不受监督的方法,这些方法学习低维维度,其中学习了初始空间的某些特性,通常是“邻里”的概念。它们是视觉化、压缩、索引化和检索等不同任务的关键组成部分。为了实现一个完全不同的目标,自我监督的视觉演示学习显示通过学习模型产生可转移的演示功能,这些模型将自上而下的自上式学习框架与人为生成的扭曲(例如,一套手工制作的图像转换)相形之下。与通常需要通过大型 k-NNN 图形或复杂的优化解决方案传播的多重学习方法不同,自上而下的学习方法依赖于更简单、更可伸缩的学习框架。在本文件中,我们从多重学习的角度将这两组方法结合起来,并提议TLDR,这是将简单自上而下的自上而下的自上而下的自上式学习框架,通过手手法来定义一套适当的扭曲。我们提议使用最近的邻居来从负式培训组合,而不是从负式的对准的学习方式进行简化的学习。