论文标题
异构对比学习：编码紧凑的视觉表示的空间信息
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
论文作者
论文摘要
对比学习在自我监督的视觉表示学习中取得了巨大的成功，但是现有的方法大多忽略了空间信息，这通常对于视觉表示至关重要。本文提出了异质性对比学习（HCL），这是一种有效的方法，它在编码阶段添加了空间信息，以减轻对比目标与强大数据增强操作之间的学习不一致。我们通过证明（i）在歧视（II）中实现了更高准确性，并且（ii）在一系列下游任务中超过现有的训练方法，同时将预训练的成本缩小一半，从而证明了HCL的有效性。更重要的是，我们表明我们的方法在视觉表示方面提高了效率更高，因此传达了一个关键信息，以激发对自我监督的视觉表示学习的未来研究。
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation. This paper presents heterogeneous contrastive learning (HCL), an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations. We demonstrate the effectiveness of HCL by showing that (i) it achieves higher accuracy in instance discrimination and (ii) it surpasses existing pre-training methods in a series of downstream tasks while shrinking the pre-training costs by half. More importantly, we show that our approach achieves higher efficiency in visual representations, and thus delivers a key message to inspire the future research of self-supervised visual representation learning.
