Contrastive instance discrimination outperforms supervised learning in downstream tasks like image classification and object detection. However, this approach heavily relies on data augmentation during representation learning, which may result in inferior results if not properly implemented. Random cropping followed by resizing is a common form of data augmentation used in contrastive learning, but it can lead to degraded representation learning if the two random crops contain distinct semantic content. To address this issue, this paper introduces LeOCLR (Leveraging Original Images for Contrastive Learning of Visual Representations), a framework that employs a new instance discrimination approach and an adapted loss function that ensures the shared region between positive pairs is semantically correct. The experimental results show that our approach consistently improves representation learning across different datasets compared to baseline models. For example, our approach outperforms MoCo-v2 by 5.1% on ImageNet-1K in linear evaluation and several other methods on transfer learning tasks.

本论文引入了LeOCLR（Leveraging Original Images for Contrastive Learning of Visual Representations），一种新的实例辨识方法和适应的损失函数，以保证正样本之间的共享区域在语义上是正确的，实验证明我们的方法在不同数据集上始终比基线模型改进了表征学习。 例如，在线性评估中，我们的方法在ImageNet-1K上比MoCo-v2提高了5.1％，在迁移学习任务中也超过了其他几种方法。

LeOCLR: 利用原始图像进行对比学习视觉表征