Momentum Contrast (MoCo) achieves great success for unsupervised visual representation. However, there are a lot of supervised and semi-supervised datasets, which are already labeled. To fully utilize the label annotations, we propose Unified Momentum Contrast (UniMoCo), which extends MoCo to support arbitrary ratios of labeled data and unlabeled data training. Compared with MoCo, UniMoCo has two modifications as follows: (1) Different from a single positive pair in MoCo, we maintain multiple positive pairs on-the-fly by comparing the query label to a label queue. (2) We propose a Unified Contrastive(UniCon) loss to support an arbitrary number of positives and negatives in a unified pair-wise optimization perspective. Our UniCon is more reasonable and powerful than the supervised contrastive loss in theory and practice. In our experiments, we pre-train multiple UniMoCo models with different ratios of ImageNet labels and evaluate the performance on various downstream tasks. Experiment results show that UniMoCo generalizes well for unsupervised, semi-supervised and supervised visual representation learning.

翻译：动态对比( Moco) 在不受监督的视觉演示中取得了巨大成功。 但是, 许多受监管和半监管的数据集已经贴上标签。 为了充分利用标签说明, 我们提议统一动态对比( UniMoco), 以扩展 Moco 支持标签数据和无标签数据培训的任意比例。 与 Moco 相比, UniMoco 有两个修改如下:(1) 不同于 Moco 的单一正对, 我们通过将查询标签与标签队列进行比较, 保持多对在飞行上的正对对。 (2) 我们提出统一对比( Unicon) 损失, 以统一对称的最佳优化观点支持任意数量的正反对。 我们的 Unicon 比在理论和实践上受监督的对比损失更加合理和有力。 在我们的实验中, 我们用图像网络标签的不同比率预排多 Unimoco 模型来评估下游任务的业绩。 实验结果显示, UniMoco 将不受监督、半监督和监督的视觉演示的学习普遍。