In this paper, we propose EqCo (Equivalent Rules for Contrastive Learning) to
make self-supervised learning irrelevant to the number of negative samples in
the contrastive learning framework. Inspired by the InfoMax principle, we point
that the margin term in contrastive loss needs to be adaptively scaled
according to the number of negative pairs in order to keep steady mutual
information bound and gradient magnitude. EqCo bridges the performance gap
among a wide range of negative sample sizes, so that for the first time, we can
use only a few negative pairs (e.g., 16 per query) to perform self-supervised
contrastive training on large-scale vision datasets like ImageNet, while with
almost no accuracy drop. This is quite a contrast to the widely used large
batch training or memory bank mechanism in current practices. Equipped with
EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCov2 on
ImageNet (linear evaluation protocol) while only involves 16 negative pairs per
query instead of 65536, suggesting that large quantities of negative samples is
not a critical factor in contrastive learning frameworks.

本文提出 EqCo（基于等效规则的对比学习），可以使用仅包含极少数量负样本（如每个查询仅包含 16 个）的自监督对比训练在大规模图像数据集（如 ImageNet）上以接近原有准确率进行，缩短了当前实践中大型批量训练或记忆库机制所需的训练时间。此外，作者提出的 simplified MoCo（SiMo）在使用较少负样本情况下在 ImageNet 上表现与 MoCov2 相当，提示负样本数量不是对比学习框架的关键因素。

EqCo: 自监督对比学习的等效规则

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper we investigate problematic practices and consequences of large
scale vision datasets. We examine broad issues such as the question of consent
and justice as well as specific concerns such as the inclusion of verifiably
pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an
example, we perform a cross-sectional model-based quantitative census covering
factors such as age, gender, NSFW content scoring, class-wise accuracy,
human-cardinality-analysis, and the semanticity of the image class information
in order to statistically investigate the extent and subtleties of ethical
transgressions. We then use the census to help hand-curate a look-up-table of
images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of
verifiably pornographic: shot in a non-consensual setting (up-skirt), beach
voyeuristic, and exposed private parts. We survey the landscape of harm and
threats both society broadly and individuals face due to uncritical and
ill-considered dataset curation practices. We then propose possible courses of
correction and critique the pros and cons of these. We have duly open-sourced
all of the code and the census meta-datasets generated in this endeavor for the
computer vision community to build on. By unveiling the severity of the
threats, our hope is to motivate the constitution of mandatory Institutional
Review Boards (IRB) for large scale dataset curation processes.

该研究调查了大规模视觉数据集的问题性实践和后果，并提出了采集数据集时应该考虑的伦理问题，包括同意和公正问题，以及诸如图像集中是否包含可验证色情图片的具体问题。同时，该论文根据实验和统计数据来阐明了数据集整理中的伦理原则和应该避免的陷阱，呼吁建立强制的伦理审查委员会。