7 papers
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…
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…
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…
POSTA: A Go-to Framework for Customized Artistic Poster Generation
Haoyu Chen, Xiaojie Xu, Wenbo Li +6
Poster design is a critical medium for visual communication. Prior work has explored automatic poster design using deep learning techniques, but these approaches lack text accuracy…
One-shot Federated Learning via Synthetic Distiller-Distillate Communication
Junyuan Zhang, Songhua Liu, Xinchao Wang
One-shot Federated learning (FL) is a powerful technology facilitating collaborative training of machine learning models in a single round of communication. While its superiority l…