no code implementations • 10 Mar 2023 • Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao, Wojciech Matusik, Leonidas Guibas
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
1 code implementation • CVPR 2021 • Mandy Lu, Qingyu Zhao, Jiequan Zhang, Kilian M. Pohl, Li Fei-Fei, Juan Carlos Niebles, Ehsan Adeli
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods.
1 code implementation • 7 Feb 2020 • Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjorn Lundh, Andrew Y. Ng
Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.