arXiv (All)
open access
CARE: Condition-Aware Representation Regularization for Diffusion Models
Recent advances in diffusion models highlight the importance of representation regularization for improving sample quality and training efficiency. However, commonly used regularization methods often overlook the built-in conditions (such as labels or texts) which directly determine the generation t
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
- Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning#1156994
- MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation#1109627
- Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale#141577
- On Trajectory-Aware Training for Masked Diffusion Language Models#1142276
- On Trajectory-Aware Training for Masked Diffusion Language Models#1122309
Record · ID 1076217
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