Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts
Fuente:
arXiv
Saved in:
| Main Authors: | Esfandiari, Yasin, Bauer, Stefan, Stich, Sebastian U., Dittadi, Andrea |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
by: Li, Bo, et al.
Published: (2023)
by: Li, Bo, et al.
Published: (2023)
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
by: Mamaghan, Amir Mohammad Karimi, et al.
Published: (2024)
by: Mamaghan, Amir Mohammad Karimi, et al.
Published: (2024)
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
by: Uselis, Arnas, et al.
Published: (2026)
by: Uselis, Arnas, et al.
Published: (2026)
DiffEnc: Variational Diffusion with a Learned Encoder
by: Nielsen, Beatrix M. G., et al.
Published: (2023)
by: Nielsen, Beatrix M. G., et al.
Published: (2023)
Are Object-Centric Representations Better At Compositional Generalization?
by: Kapl, Ferdinand, et al.
Published: (2026)
by: Kapl, Ferdinand, et al.
Published: (2026)
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
by: Tang, Anke, et al.
Published: (2024)
by: Tang, Anke, et al.
Published: (2024)
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
by: Shen, Li, et al.
Published: (2024)
by: Shen, Li, et al.
Published: (2024)
Pretrained Diffusion Models Are Inherently Skipped-Step Samplers
by: Xu, Wenju
Published: (2025)
by: Xu, Wenju
Published: (2025)
Efficient Multi-Source Knowledge Transfer by Model Merging
by: Osial, Marcin, et al.
Published: (2025)
by: Osial, Marcin, et al.
Published: (2025)
Diffusion-Based Representation Learning
by: Mittal, Sarthak, et al.
Published: (2021)
by: Mittal, Sarthak, et al.
Published: (2021)
MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
by: Qiu, Zihuan, et al.
Published: (2025)
by: Qiu, Zihuan, et al.
Published: (2025)
Medical Semantic Segmentation with Diffusion Pretrain
by: Li, David, et al.
Published: (2025)
by: Li, David, et al.
Published: (2025)
DC-Merge: Improving Model Merging with Directional Consistency
by: Zhang, Han-Chen, et al.
Published: (2026)
by: Zhang, Han-Chen, et al.
Published: (2026)
AdaMerging: Adaptive Model Merging for Multi-Task Learning
by: Yang, Enneng, et al.
Published: (2023)
by: Yang, Enneng, et al.
Published: (2023)
EMR-Merging: Tuning-Free High-Performance Model Merging
by: Huang, Chenyu, et al.
Published: (2024)
by: Huang, Chenyu, et al.
Published: (2024)
Assessing Neural Network Robustness via Adversarial Pivotal Tuning
by: Christensen, Peter Ebert, et al.
Published: (2022)
by: Christensen, Peter Ebert, et al.
Published: (2022)
Quantile-based Maximum Likelihood Training for Outlier Detection
by: Taghikhah, Masoud, et al.
Published: (2023)
by: Taghikhah, Masoud, et al.
Published: (2023)
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks
by: Vasu, Pavan Kumar Anasosalu, et al.
Published: (2024)
by: Vasu, Pavan Kumar Anasosalu, et al.
Published: (2024)
Saliency-Aware Model Merging
by: Park, Jungin, et al.
Published: (2026)
by: Park, Jungin, et al.
Published: (2026)
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off
by: Belousov, Yury, et al.
Published: (2025)
by: Belousov, Yury, et al.
Published: (2025)
Efficient Training of Diffusion Mixture-of-Experts Models: A Practical Recipe
by: Liu, Yahui, et al.
Published: (2025)
by: Liu, Yahui, et al.
Published: (2025)
MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics
by: Guo, Bowei, et al.
Published: (2025)
by: Guo, Bowei, et al.
Published: (2025)
How to Merge Your Multimodal Models Over Time?
by: Dziadzio, Sebastian, et al.
Published: (2024)
by: Dziadzio, Sebastian, et al.
Published: (2024)
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
by: Yang, Huitong, et al.
Published: (2025)
by: Yang, Huitong, et al.
Published: (2025)
Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble
by: Wang, Jihang, et al.
Published: (2025)
by: Wang, Jihang, et al.
Published: (2025)
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
by: Lu, Aojun, et al.
Published: (2025)
by: Lu, Aojun, et al.
Published: (2025)
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
by: Zhang, Jiacheng, et al.
Published: (2024)
by: Zhang, Jiacheng, et al.
Published: (2024)
Benchmarking the Attribution Quality of Vision Models
by: Hesse, Robin, et al.
Published: (2024)
by: Hesse, Robin, et al.
Published: (2024)
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
by: Chen, Tianyu, et al.
Published: (2025)
by: Chen, Tianyu, et al.
Published: (2025)
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
by: Joshi, Siddharth, et al.
Published: (2024)
by: Joshi, Siddharth, et al.
Published: (2024)
Simplifying Knowledge Transfer in Pretrained Models
by: Jain, Siddharth, et al.
Published: (2025)
by: Jain, Siddharth, et al.
Published: (2025)
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
by: Ding, Yifan, et al.
Published: (2025)
by: Ding, Yifan, et al.
Published: (2025)
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation
by: Zhao, Shiji, et al.
Published: (2023)
by: Zhao, Shiji, et al.
Published: (2023)
AgriPath: A Systematic Exploration of Architectural Trade-offs for Crop Disease Classification
by: Mooraj, Hamza, et al.
Published: (2026)
by: Mooraj, Hamza, et al.
Published: (2026)
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
by: Kaur, Amandeep, et al.
Published: (2026)
by: Kaur, Amandeep, et al.
Published: (2026)
Toward Early Quality Assessment of Text-to-Image Diffusion Models
by: Guo, Huanlei, et al.
Published: (2026)
by: Guo, Huanlei, et al.
Published: (2026)
Data Pruning in Generative Diffusion Models
by: Briq, Rania, et al.
Published: (2024)
by: Briq, Rania, et al.
Published: (2024)
Localizing Task Information for Improved Model Merging and Compression
by: Wang, Ke, et al.
Published: (2024)
by: Wang, Ke, et al.
Published: (2024)
CubistMerge: Spatial-Preserving Token Merging For Diverse ViT Backbones
by: Gong, Wenyi, et al.
Published: (2025)
by: Gong, Wenyi, et al.
Published: (2025)
MagMax: Leveraging Model Merging for Seamless Continual Learning
by: Marczak, Daniel, et al.
Published: (2024)
by: Marczak, Daniel, et al.
Published: (2024)
Similar Items
-
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
by: Li, Bo, et al.
Published: (2023) -
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
by: Mamaghan, Amir Mohammad Karimi, et al.
Published: (2024) -
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
by: Uselis, Arnas, et al.
Published: (2026) -
DiffEnc: Variational Diffusion with a Learned Encoder
by: Nielsen, Beatrix M. G., et al.
Published: (2023) -
Are Object-Centric Representations Better At Compositional Generalization?
by: Kapl, Ferdinand, et al.
Published: (2026)