We propose a unifying view to analyze the representation quality of
self-supervised learning (SSL) models without access to supervised labels,
while being agnostic to the architecture, learning algorithm or data
manipulation used during training. We argue that representations can be
evaluated through the lens of expressiveness and learnability. We propose to
use the Intrinsic Dimension (ID) to assess expressiveness and introduce Cluster
Learnability (CL) to assess learnability. CL is measured as the learning speed
of a KNN classifier trained to predict labels obtained by clustering the
representations with K-means. We thus combine CL and ID into a single
predictor: CLID. Through a large-scale empirical study with a diverse family of
SSL algorithms, we find that CLID better correlates with in-distribution model
performance than other competing recent evaluation schemes. We also benchmark
CLID on out-of-domain generalization, where CLID serves as a predictor of the
transfer performance of SSL models on several classification tasks, yielding
improvements with respect to the competing baselines.

本文研究了自监督学习（SSL）模型的表征质量，并提出了一种将表现力和可学习性作为评估依据的方法。 作者将内在维度（ID）用于评估表现力并引入了聚类可学习性（CL）来评估可学习性。作者发现，将 CL 和 ID 组合成单个预测器（CLID）比其他竞争的最近的评估方案更能与分布内模型的表现力相关联，并在多个分类任务上取得了显着的性能提升。