GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
<|reference_start|>GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning: Self-supervised learning has achieved remarkable success in acquiring high-quality representations from unlabeled data. The widely adopted contrastive learning framework aims to learn invariant representations by minimizing the distance between positive views originating from the same image. However, existing techniques to construct positive views highly rely on manual transformations, resulting in limited diversity and potentially false positive pairs. To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics. We develop an adaptive view generation method that dynamically adjusts the noise level in sampling to ensure the preservation of essential semantic meaning while introducing variability. Additionally, we introduce a quality-driven contrastive loss, which assesses the quality of positive pairs by considering both foreground similarity and background diversity. This loss prioritizes the high-quality positive pairs we construct while reducing the influence of low-quality pairs, thereby mitigating potential semantic inconsistencies introduced by generative models and aggressive data augmentation. Thanks to the improved positive view quality and the quality-driven contrastive loss, GenView significantly improves self-supervised learning across various tasks. For instance, GenView improves MoCov2 performance by 2.5%/2.2% on ImageNet linear/semi-supervised classification. Moreover, GenView even performs much better than naively augmenting the ImageNet dataset with Laion400M or ImageNet21K. Code is available at https://github.com/xiaojieli0903/genview.<|reference_end|>
@article{li2024genview:,
    title={GenView: Enhancing View Quality with Pretrained Generative Model for
  Self-Supervised Learning},
    author={Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu, Yue Yu, Bernard
  Ghanem, Min Zhang},
    journal={arXiv preprint arXiv:2403.12003},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12003},
    primaryClass={cs.CV}
}