Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Marginal Matching Does Not License Factorized Sampling: Auditing Conditional Style Leakage in Factorized Generative Models
TL;DR
Factorized generative models often misinterpret matching a latent style variable's marginal distribution to a Gaussian prior as ensuring independence from class labels. This study reveals that such marginal matching does not prevent class information leakage, leading to misleading performance metrics.
✦ Why It Matters
Reassess your use of marginal statistics in generative models to ensure accurate independence claims.
Key Takeaways
- Do not rely solely on marginal statistics for independence claims.
- Evaluate class-conditional distributions alongside marginal distributions.
- Implement mitigation strategies to address class information leakage.
- Consider using empirical style banks for improved generation performance.
How It Works
The study reveals that matching the marginal distribution of a latent style variable to a Gaussian prior does not constrain its relationship with class labels. This means that while the overall distribution may appear Gaussian, the style variable can still carry significant class information, leading to misleading interpretations of independence.