论文标题
使用表示表现力和学习性来评估自我监督的学习方法
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
论文作者
论文摘要
我们解决了评估自学学习模型（SSL）模型质量的问题，而无需使用监督标签，同时对架构不可知，学习算法或培训期间使用的数据操纵。我们认为可以通过表现力和可学习性的角度来评估表示形式。我们建议使用固有维度（ID）评估表现力并引入集群可学习性（CL）以评估可学习性。 Cl是根据训练有素的KNN分类器的性能来测量CL，该分类器通过用K-均值聚类来预测获得的标签。因此，我们将Cl和ID组合到单个预测因子中 - 固定。通过一项大规模的实证研究，具有多种SSL算法系列，我们发现，与其他竞争的最近评估方案相比，CLID与分配模型的性能更好。我们还基于跨域的概括为基础，其中CLID是SSL模型在几个视觉分类任务上转移性能的预测指标，从而相对于竞争基线而进行了改进。
We address the problem of evaluating the 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 in terms of the performance 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 visual classification tasks, yielding improvements with respect to the competing baselines.
