Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
Marzi Heidari Hanping Zhang Yuhong Guo
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.