Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment
The high cost and accessibility problem associated with large datasets hinder the development of large-scale visual recognition systems. Dataset Distillation addresses these problems by synthesizing compact surrogate datasets for efficient training, storage, transfer, and privacy preservation. The existing state-of-the-art diffusion-based dataset distillation methods face three issues: lack of theoretical justification, poor efficiency in scaling to high data volumes, and failure in data-free sc
Record details
Published: 30 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Privacy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
WiFi2Cap: Semantic Action Captioning from Wi-Fi CSI via Limb-Level Semantic Alignment
arXiv · 24 March 2026
A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs
arXiv · 6 April 2026
Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
arXiv · 22 March 2026
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation
arXiv · 8 April 2026
Characterizing Linear Alignment Across Language Models
arXiv · 19 March 2026
Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition
arXiv · 10 April 2026
How to cite this record
ethics.ai (30 March 2026), “Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment,” evidence record 6609, https://ethics.ai/record/6609 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.