Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level performance on a variety of academic benchmarks, it is still hard to train and evaluate SSL models in practice due to the unsupervised nature of the problem. Even with networks trained in a supervised fashion, it is often unclear whether they will perform well when transferred to another domain. Past works are generally limited to assessing the amount of information contained in embeddings, which is most relevant for self-supervised learning of deep neural networks. This works chooses to follow a different approach: can we quantify how easy it is to linearly separate the data in a stable way? We survey the literature and uncover three methods that could be potentially used for evaluating quality of representations. We also introduce one novel method based on recent advances in understanding the high-dimensional geometric structure of self-supervised learning. We conduct extensive experiments and study the properties of these metrics and ones introduced in the previous work. Our results suggest that while there is no free lunch, there are metrics that can robustly estimate embedding quality in an unsupervised way.

翻译：无监督学习近期显著受到关注，尤其是基于深度学习的方法。尽管在多项学术基准测试中取得了诸多成功，并接近监督学习的性能水平，但由于问题的无监督特性，在实际中训练和评估自监督学习模型仍然困难。即使以监督方式训练的网络，其在迁移至其他领域时是否表现良好也常常不明确。以往的工作通常局限于评估嵌入中包含的信息量，这对于深度神经网络的自监督学习最为关键。本研究选择了一条不同的路径：能否量化在稳定方式下对数据进行线性分离的难易程度？我们调研了相关文献，发现三种可能用于评估表示质量的方法，并基于自监督学习高维几何结构理解的最新进展，提出了一种新方法。我们开展了广泛实验，研究了这些度量指标及先前工作中引入的度量的特性。结果表明，尽管没有免费的午餐，但存在能够以无监督方式稳健估计嵌入质量的度量指标。