arXiv (All)
open access
Memorization and Malign Generalization in Conditional Diffusion Models with Random Features
Conditional diffusion models generate diverse, novel, and high-quality samples under prescribed conditions. However, theoretical understanding of their memorization and generalization remains limited, while recent works have characterized these behaviors primarily in unconditional settings. In this
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GRACE: Adaptive Concept Erasure with Geometry-Guided Retention in Diffusion Models#1070205
- Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting#1142986
- Local Diffusion Models and Phases of Data Distributions#158764
- Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry#627622
- Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo#1155330
Record · ID 1248568
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive