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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs
TL;DR
Function-symbol grounding in Logic Tensor Network-Enhanced Generative Adversarial Networks (LTN-GANs) allows for the embedding of hard constraints directly into the generation process, improving the realism of generated samples. This method overcomes limitations of previous approaches that only scored samples against constraints, which often led to unrealistic outputs.
✦ Why It Matters
Consider implementing function-symbol grounding in your LTN-GANs to enhance the realism of generated outputs.
Key Takeaways
- Use function symbols to embed hard constraints in LTN-GANs.
- Evaluate the resolution ratio to assess constraint learning potential.
- Avoid relying solely on predicates for constraint satisfaction.
- Ensure generated samples maintain both validity and realism.
How It Works
Grounding logical axioms as function symbols allows the generator to compute constrained variables directly, rather than merely scoring them. This method constructs a coordinate system within the feasible region, ensuring all generated samples are valid and capturing the distribution of margins effectively.