By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field - many methods with substantially different implementations yield results that seem nearly identical on popular benchmarks, such as linear evaluation on ImageNet. However, a single result does not tell the whole story. In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear front-runner within the current state-of-the-art. In contrast to prior work that performs only supervised vs. unsupervised comparison, we compare several different unsupervised methods against each other. To enrich this comparison, we analyze embeddings with measurements such as uniformity, tolerance, and centered kernel alignment (CKA), and propose two new metrics of our own: nearest neighbor graph similarity and linear prediction overlap. We reveal through our analysis that in isolation, single popular methods should not be treated as though they represent the field as a whole, and that future work ought to consider how to leverage the complimentary nature of these methods. We also leverage CKA to provide a framework to robustly quantify augmentation invariance, and provide a reminder that certain types of invariance will be undesirable for downstream tasks.

本文提供一个综合性的比较不同无监督学习方法在图像特征表达方面的表现，使用线性评估、最近邻分类和聚类等多个基准测试来定量比较，分析了不同嵌入度量的均匀性、容忍性和中心内核对齐，并提出了两个新的度量。通过比较分析发现不能以单一流行的方法来代表整个领域的研究，在未来的工作中，应考虑如何利用这些方法的互补性。同时提供了一个统一的框架来定量数据增强不变性，并提醒不同任务需要的增强不变性类型各有差异。

超越有监督与无监督：图像表示学习的代表性基准测试与分析