LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations

arXiv:2403.06813v1 Announce Type: new
Abstract: 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 …

abstract arxiv augmentation classification cs.cv data detection discrimination form however image images instance object random representation representation learning results supervised learning tasks type visual