Research Paper#Generative Models, Classification, Distribution Shift🔬 ResearchAnalyzed: Jan 3, 2026 06:13
Generative Classifiers Outperform Discriminative Ones on Distribution Shift
Published:Dec 31, 2025 18:31
•1 min read
•ArXiv
Analysis
This paper addresses a critical problem in machine learning: the vulnerability of discriminative classifiers to distribution shifts due to their reliance on spurious correlations. It proposes and demonstrates the effectiveness of generative classifiers as a more robust alternative. The paper's significance lies in its potential to improve the reliability and generalizability of AI models, especially in real-world applications where data distributions can vary.
Key Takeaways
- •Discriminative classifiers often fail under distribution shift due to reliance on spurious correlations.
- •Generative classifiers, using class-conditional generative models, are proposed as a more robust alternative.
- •Diffusion-based and autoregressive generative classifiers achieve state-of-the-art performance on distribution shift benchmarks.
- •Generative classifiers reduce the impact of spurious correlations in realistic applications.
- •The paper provides analysis of generative classifier inductive biases and data properties for optimal performance.
Reference
“Generative classifiers...can avoid this issue by modeling all features, both core and spurious, instead of mainly spurious ones.”