1 citations · 1 across the 7 of their papers we have counts for
7 papers
Exploring 3D Dataset Pruning
Xiaohan Zhao, Xinyi Shang, Jiacheng Liu +1
Dataset pruning has been widely studied for 2D images to remove redundancy and accelerate training, while particular pruning methods for 3D data remain largely unexplored. In this…
DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation
Zhiqiang Shen, Ammar Sherif, Zeyuan Yin +1
Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and inclu…
Self-supervised Dataset Distillation: A Good Compression Is All You Need
Muxin Zhou, Zeyuan Yin, Shitong Shao +1
Dataset distillation aims to compress information from a large-scale original dataset to a new compact dataset while striving to preserve the utmost degree of the original data inf…
Elucidating the Design Space of Dataset Condensation
Shitong Shao, Zikai Zhou, Huanran Chen +1
Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity…
Precise Knowledge Transfer via Flow Matching
Shitong Shao, Zhiqiang Shen, Linrui Gong +2
In this paper, we propose a novel knowledge transfer framework that introduces continuous normalizing flows for progressive knowledge transformation and leverages multi-step sampli…
Dataset Distillation via Curriculum Data Synthesis in Large Data Era
Zeyuan Yin, Zhiqiang Shen
Dataset distillation or condensation aims to generate a smaller but representative subset from a large dataset, which allows a model to be trained more efficiently, meanwhile evalu…