Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance

5 March 2022

Papers citing "Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance"

8 / 8 papers shown

Title
Sparse-to-Sparse Training of Diffusion Models Inês Cardoso Oliveira Decebal Constantin Mocanu Luis A. Leiva DiffM 81 0 0 30 Apr 2025
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning Zhongzhi Yu Yang Zhang Kaizhi Qian Y. Fu Yingyan Lin 23 13 0 23 Jun 2023
Balanced Training for Sparse GANs Yite Wang Jing Wu N. Hovakimyan Ruoyu Sun 32 9 0 28 Feb 2023
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training Rong Dai Li Shen Fengxiang He Xinmei Tian Dacheng Tao FedML 11 111 0 01 Jun 2022
Carbon Emissions and Large Neural Network Training David A. Patterson Joseph E. Gonzalez Quoc V. Le Chen Liang Lluís-Miquel Munguía D. Rothchild David R. So Maud Texier J. Dean AI4CE 239 642 0 21 Apr 2021
Truly Sparse Neural Networks at Scale Selima Curci D. Mocanu Mykola Pechenizkiy 18 19 0 02 Feb 2021
Distilling portable Generative Adversarial Networks for Image Translation Hanting Chen Yunhe Wang Han Shu Changyuan Wen Chunjing Xu Boxin Shi Chao Xu Chang Xu 78 83 0 07 Mar 2020
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 262 10,344 0 12 Dec 2018