Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
<|reference_start|>Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning: In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficient feature-level diffusion model with the guidance of class-specific prompts to support discriminative feature representation learning and feature generation, tackling the challenge of the non-availability of labeled data for unseen classes in OW-SSL. In particular, PDFD utilizes class prototypes as prompts in the diffusion model, leveraging their class-discriminative and semantic generalization ability to condition and guide the diffusion process across all the seen and unseen classes. Furthermore, PDFD incorporates a class-conditional adversarial loss for diffusion model training, ensuring that the features generated via the diffusion process can be discriminatively aligned with the class-conditional features of the real data. Additionally, the class prototypes of the unseen classes are computed using only unlabeled instances with confident predictions within a semi-supervised learning framework. We conduct extensive experiments to evaluate the proposed PDFD. The empirical results show PDFD exhibits remarkable performance enhancements over many state-of-the-art existing methods.<|reference_end|>
@article{heidari2024prompt-driven,
    title={Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning},
    author={Marzi Heidari, Hanping Zhang, Yuhong Guo},
    journal={arXiv preprint arXiv:2404.11795},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11795},
    primaryClass={cs.LG cs.AI cs.CV}
}