8 citations · 8 across the 8 of their papers we have counts for
6 papers · 1 filter
CoDA: From Text-to-Image Diffusion Models to Training-Free Dataset Distillation
Letian Zhou, Songhua Liu, Xinchao Wang
Prevailing Dataset Distillation (DD) methods leveraging generative models confront two fundamental limitations. First, despite pioneering the use of diffusion models in DD and deli…
Control and Realism: Best of Both Worlds in Layout-to-Image without Training
Bonan Li, Yinhan Hu, Songhua Liu +1
Layout-to-Image generation aims to create complex scenes with precise control over the placement and arrangement of subjects. Existing works have demonstrated that pre-trained Text…
Understanding Dataset Distillation via Spectral Filtering
Deyu Bo, Songhua Liu, Xinchao Wang
Dataset distillation (DD) has emerged as a promising approach to compress datasets and speed up model training. However, the underlying connections among various DD methods remain…
Unifying Dataset Pruning and Distillation for Efficient Large-scale Compression
Lingao Xiao, Songhua Liu, Yang He +1
Dataset pruning (DP) and dataset distillation (DD) fundamentally differ in their outputs: DP selects original image subsets, while DD generates synthetic images. Recently, DD's inc…
Teddy: Efficient Large-Scale Dataset Distillation via Taylor-Approximated Matching
Ruonan Yu, Songhua Liu, Jingwen Ye +1
Dataset distillation or condensation refers to compressing a large-scale dataset into a much smaller one, enabling models trained on this synthetic dataset to generalize effectivel…
CAFE: Learning to Condense Dataset by Aligning Features
Kai Wang, Bo Zhao, Xiangyu Peng +7
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rel…