no code implementations • 31 May 2022 • Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng
The invariance may degrade the representation learning ability of the discriminator, thereby affecting the generative modeling performance of the generator.
no code implementations • 25 Apr 2022 • Siyuan Pan, Xiaoshuang Li, Tingyao Li, Liang Hou, KaiBin Qiu, Xiaobing Tu
To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning.
2 code implementations • 21 Jul 2021 • Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on
Conditional Image Generation
on Tiny ImageNet