MGPO: Manifold-Guided Diffusion Alignment for Task-Aware Dataset Distillation
cs.CV, cs.AI
Submitted: 2026-10-04
Updated: 2026-10-04
Code: https://github.com/fastai/imagenette
Terminology
Sources
- MGD$^3$: Mode-Guided Dataset Distillation using Diffusion Models
- Gradient Guidance for Diffusion Models: An Optimization Perspective
- Denoising Diffusion Probabilistic Models
- Searching for MobileNetV3
- Diffusion Models as Cartoonists: The Curious Case of High Density Regions
- Dataset Condensation via Efficient Synthetic-Data Parameterization
- MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
- BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models
- Microsoft COCO: Common Objects in Context
- GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
- Scalable Diffusion Models with Transformers
- ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
- Active Learning for Convolutional Neural Networks: A Core-Set Approach
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Denoising Diffusion Implicit Models
- Dataset Distillation
- DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
- DanceGRPO: Unleashing GRPO on Visual Generation
- Dataset Condensation with Gradient Matching
- Taming Diffusion for Dataset Distillation with High Representativeness
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