Latent-based Diffusion Model for Long-tailed Recognition
<|reference_start|>Latent-based Diffusion Model for Long-tailed Recognition: Long-tailed imbalance distribution is a common issue in practical computer vision applications. Previous works proposed methods to address this problem, which can be categorized into several classes: re-sampling, re-weighting, transfer learning, and feature augmentation. In recent years, diffusion models have shown an impressive generation ability in many sub-problems of deep computer vision. However, its powerful generation has not been explored in long-tailed problems. We propose a new approach, the Latent-based Diffusion Model for Long-tailed Recognition (LDMLR), as a feature augmentation method to tackle the issue. First, we encode the imbalanced dataset into features using the baseline model. Then, we train a Denoising Diffusion Implicit Model (DDIM) using these encoded features to generate pseudo-features. Finally, we train the classifier using the encoded and pseudo-features from the previous two steps. The model's accuracy shows an improvement on the CIFAR-LT and ImageNet-LT datasets by using the proposed method.<|reference_end|>
@article{han2024latent-based,
    title={Latent-based Diffusion Model for Long-tailed Recognition},
    author={Pengxiao Han, Changkun Ye, Jieming Zhou, Jing Zhang, Jie Hong, Xuesong
  Li},
    journal={arXiv preprint arXiv:2404.04517},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04517},
    primaryClass={cs.CV cs.AI}
}