dataset distillation
Dataset distillation is a process of creating a smaller, more efficient dataset that captures the essential information from a larger dataset, often used to enhance training efficiency and reduce computational costs.
- Beyond Modality Collapse: Representation Blending for Multimodal Dataset Distillation
- Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
- Dataset Distillation for Pre-Trained Self-Supervised Vision Models
- Dataset Distillation of 3D Point Clouds via Distribution Matching
- Efficient Multimodal Dataset Distillation via Generative Models
- FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
- FairDD: Fair Dataset Distillation
- Hyperbolic Dataset Distillation
- Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation
- Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation
- Unlocking Dataset Distillation with Diffusion Models