Thorray

ThorrayApollo

上海，中国 上海交通大学 @Thorray

  1. 数据结构：Trie树

    2020/08/14

  2. 论文阅读-17：多态自监督引导退化显著性目标检测

    2020/07/30

  3. 论文阅读-16：学习深度关系提升显著性目标检测

    2020/07/29

  4. 论文阅读-15：深度诱发多尺寸循环注意力网络

    2020/05/05

  5. 论文阅读-14：DFNet:显著目标检测的判别特征提取与集成网络

    2020/04/01

  6. 论文阅读-13：深度视觉注意力预测

    2020/03/24

  7. 论文阅读-12：用于显著性目标检测的渐进特征抛光网络

    2020/03/09

  8. 深度学习1：常用Normalization方法总结

    2020/03/07

  9. 论文阅读-11：F3Net：用于显著性目标检测融合，反馈，聚焦

    2020/03/04

  10. 从点子到产品：产品经理的价值观和方法论（一）

    2020/02/28

My Popular Repositories

Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection

There is a paper in CVPR 2019 about the saliency detection. The source code of this paper is using the keras framework. We transform them into pytorch framework.

9 2

CS137-TA

4 0

2019-Postgraduate-Mathematical-Modeling-Competition-A

无线智能传播模型 ：利用模型准确预测在新环境下无线信号覆盖强度，减少网络 建设成本，提高网络建设效率

3 2

Big-Data-Processing

This course mainly introduces big data storage systems. For both structured and unstructured data, this course illustrates them in storage and database tracks, respectively. Particularly, tranditional big data storage systems are introduced in this course, such as hadoop (HDFS,HBase), Google GFS/Chubby/BigTable, Amazon S3, Microsoft Azure Storage, Ali Cloud, etc. Critical issues like data reliability, data consistency, metadata management are well illustrated in this course. The course aims to help students to understand how file systems/databases really work for big data storage.

2 0

thorraysjtu.github.io

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