LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations
<|reference_start|>LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations: Contrastive instance discrimination approaches outperform supervised learning in downstream tasks like image classification and object detection. However, these approaches heavily rely 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 to alleviate discarding semantic features caused by mapping different object parts during representation learning. 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.<|reference_end|>
@article{alkhalefi2024leoclr:,
    title={LeOCLR: Leveraging Original Images for Contrastive Learning of Visual
  Representations},
    author={Mohammad Alkhalefi, Georgios Leontidis, and Mingjun Zhong},
    journal={TMLR; 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06813},
    primaryClass={cs.CV}
}