arXiv:2606.09718v1 Announce Type: new Abstract: Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet the connection between these two abilities remains less explored. Drawing inspiration from self-supervised learning (SSL), we introduce a framework for jointly evaluating the representation and generation capabilities of diffusion models. Specifically, we decompose features into invariant and residual components and derive the Invariant Contamination Ratio (ICR), a Fisher-based metric that quantifies
The rapid advancement and widespread application of diffusion models necessitate better methods for evaluating their underlying capabilities as both generative tools and representation learners.
A deeper understanding and quantification of diffusion model representation spaces can lead to more efficient, robust, and controllable AI systems, impacting virtually all AI-driven applications.
The proposed framework and metric provide a new analytical lens for assessing the dual nature of diffusion models, moving beyond purely generative metrics to also quantify their efficacy as self-supervised learners.
- · AI researchers
- · Machine learning platforms
- · Generative AI developers
- · Development teams relying solely on subjective generative evaluation
Improved evaluation metrics for diffusion models will accelerate their development and optimization for both generative and representational tasks.
This could lead to more robust and less 'hallucinatory' generative AI, as well as more powerful self-supervised learning for downstream tasks.
Enhanced self-supervised representation learning from diffusion models might reduce the need for massive labeled datasets, accelerating AI deployment in data-scarce domains.
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Read at arXiv cs.LG