Contrastive self-supervised learning (CSL) is an approach to learn useful
representations by solving a pretext task that selects and compares anchor,
negative and positive (APN) features from an unlabeled dataset. We present a
conceptual framework that characterizes CSL approaches in five aspects (1) data
augmentation pipeline, (2) encoder selection, (3) representation extraction,
(4) similarity measure, and (5) loss function. We analyze three leading CSL
approaches--AMDIM, CPC, and SimCLR--, and show that despite different
motivations, they are special cases under this framework. We show the utility
of our framework by designing Yet Another DIM (YADIM) which achieves
competitive results on CIFAR-10, STL-10 and ImageNet, and is more robust to the
choice of encoder and the representation extraction strategy. To support
ongoing CSL research, we release the PyTorch implementation of this conceptual
framework along with standardized implementations of AMDIM, CPC (V2), SimCLR,
BYOL, Moco (V2) and YADIM.

通过解决先验任务从无标签数据集中选择和比较 anchor、negative 和 positive 特征来学习有用的表示，我们提出了一个概念性框架，该框架从数据增强管道、编码器选择、表示提取、相似性度量和损失函数五个方面表征对比自监督学习方法。我们分析了三种主要的 CSL 方法 - AMDIM、CPC 和 SimCLR-，并表明它们是这个框架下的特殊情况。通过设计 Yet Another DIM（YADIM），我们展示了我们框架的效用，该方法在 CIFAR-10、STL-10 和 ImageNet 上取得了竞争性结果，并且对编码器的选择和表示提取策略更具鲁棒性。为了支持正在进行的 CSL 研究，我们发布了此概念框架的 PyTorch 实现，以及 AMDIM、CPC（V2）、SimCLR、BYOL、Moco（V2）和 YADIM 的标准化实现。