Summary of Prompt-driven Feature Diffusion For Open-world Semi-supervised Learning, by Marzi Heidari et al.
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
by Marzi Heidari, Hanping Zhang, Yuhong Guo
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents Prompt-Driven Feature Diffusion (PDFD), a novel approach for Open World Semi-Supervised Learning (OW-SSL). PDFD uses an efficient feature-level diffusion model guided by class-specific prompts to learn discriminative features and generate new ones. The prompts, based on class prototypes, help condition the diffusion process across seen and unseen classes. Additionally, PDFD incorporates a class-conditional adversarial loss for training the diffusion model. Experimental results show significant performance enhancements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces Prompt-Driven Feature Diffusion (PDFD), a new way to learn from data without labels. Imagine you’re trying to recognize pictures of animals, but some animals are not labeled. PDFD helps by creating a better representation of what animals look like and can even generate new animal images that fit the pattern. It’s like a special kind of AI artist! The results show that this approach works much better than other methods. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Prompt » Semi supervised
