Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
arXiv:2606.00514v1 Announce Type: new Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rewards semantic coherence. Yet recent work repeatedly finds that SSL features improve generative training, though the mechanism of this synergy remains unclear. Here, we study the benefits of SSL in generative modeling in the framework of one-step generation where the role of representation is explicit: frozen SSL features are used to match generated samples to real data.
The rapid advancement in both generative AI and self-supervised learning has created an environment ripe for exploring their synergistic potential, leading to new methodological breakthroughs.
This development proposes a more efficient and semantically coherent approach to generative modeling, potentially addressing current limitations in content generation and synthetic data creation.
The explicit integration of frozen self-supervised learning features into one-step generative processes fundamentally alters how generative models are designed and optimized, moving towards more semantically aware outputs.
- · AI researchers
- · Generative AI developers
- · Content creation industries
- · High-fidelity simulation platforms
- · Generative models reliant solely on distributional fidelity
- · Current methods for synthetic data generation without strong semantic anchors
Improved generative models capable of producing more semantically consistent and diverse content.
Reduced computational costs and accelerated development cycles for sophisticated AI applications requiring high-quality synthetic data.
Enhanced AI agents and autonomous systems that can generate more nuanced and context-aware outputs, accelerating their adoption across industries.
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Read at arXiv cs.LG