DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification

no code implementations • 25 May 2023 • Sitian Shen, Zilin Zhu, Linqian Fan, Harry Zhang, Xinxiao wu

Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation.

3D Classification Classification +5

WeLM: A Well-Read Pre-trained Language Model for Chinese

no code implementations • 21 Sep 2022 • Hui Su, Xiao Zhou, Houjin Yu, Xiaoyu Shen, YuWen Chen, Zilin Zhu, Yang Yu, Jie zhou

Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.

Language Modelling Self-Supervised Learning +2

PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management

1 code implementation • 12 Aug 2021 • Jiarui Fang, Zilin Zhu, Shenggui Li, Hui Su, Yang Yu, Jie zhou, Yang You

PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data.

Management

Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters

no code implementations • 20 Oct 2020 • Shaohuai Shi, Xianhao Zhou, Shutao Song, Xingyao Wang, Zilin Zhu, Xue Huang, Xinan Jiang, Feihu Zhou, Zhenyu Guo, Liqiang Xie, Rui Lan, Xianbin Ouyang, Yan Zhang, Jieqian Wei, Jing Gong, Weiliang Lin, Ping Gao, Peng Meng, Xiaomin Xu, Chenyang Guo, Bo Yang, Zhibo Chen, Yongjian Wu, Xiaowen Chu

Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.