LeOCLR: Leveraging Original Images for Contrastive Learning of Visual
  Representations
Mohammad Alkhalefi Georgios Leontidis Mingjun Zhong
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.