Not All Samples Should Be Utilized Equally: Towards Understanding and
Improving Dataset Distillation

Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation

22 August 2024

Yantai Yang

Papers citing "Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation"

7 / 7 papers shown

Title
FairDD: Fair Dataset Distillation via Synchronized Matching Qihang Zhou Shenhao Fang Shibo He Wenchao Meng Jiming Chen FedML DD 77 1 0 29 Nov 2024
DRUPI: Dataset Reduction Using Privileged Information Shaobo Wang Yantai Yang Shuaiyu Zhang Chenghao Sun Weiya Li Xuming Hu Linfeng Zhang 30 0 0 02 Oct 2024
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective Ming-Yu Chung Sheng-Yen Chou Chia-Mu Yu Pin-Yu Chen Sy-Yen Kuo Tsung-Yi Ho DD 37 6 0 28 Nov 2023
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation Chun-Yin Huang Rui Jin Can Zhao Daguang Xu Xiaoxiao Li FedML DD 41 7 0 04 Mar 2023
Dataset Condensation via Efficient Synthetic-Data Parameterization Jang-Hyun Kim Jinuk Kim Seong Joon Oh Sangdoo Yun Hwanjun Song Joonhyun Jeong Jung-Woo Ha Hyun Oh Song DD 378 155 0 30 May 2022
DEAL: Difficulty-aware Active Learning for Semantic Segmentation Shuai Xie Zunlei Feng Ying Chen Songtao Sun Chao Ma Mingli Song VLM 107 50 0 17 Oct 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020