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[doing] Understanding Deep Learning Techniques for Image Segmentation

浏览 58 扫码 分享 2022-07-26 13:05:38

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  • 书签
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  • 01 - Knowledge Tree
  • 02 - Papers
    • Graph Neural Networks (GNNs)
      • GNNs模型总结
      • GNNs系统总结
      • ICLR 2020 GNNs相关论文
        • Curvature Graph Network
      • NIPS 2020 GNNs相关论文
      • ICLR 2021 GNNs相关论文
        • How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
      • NIPS 2021 GNNs相关论文
        • PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
      • Learning on Graphs Conference
      • GNN解偏微分方程
      • A Comprehensive Survey on Graph Neural Networks
      • Graph ML in 2022: Where Are We Now?
      • Do Transformers Really Perform Bad for Graph Representation?
      • A Tutorial on Network Embeddings
      • Graph Neural Networks: A Review of Methods and Applications
    • Self-Supervised Learning (SSL)
      • Self-Supervised Learning 入门介绍
      • [todo] Self-supervised Learning: Generative or Contrastive
    • Reinforcement Learning (RL)
      • Model-based Reinforcement Learning: A Survey
    • Bayesian Deep Learning (BDL)
      • A Survey on Bayesian Deep Learning.
    • Others
      • [doing] Understanding Deep Learning Techniques for Image Segmentation
      • [todo] Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
      • [todo] A Survey on Multi-Task Learning
      • Automatic differentiation in machine learning: a survey
      • Characterization of Complex Networks: A Survey of measurements
      • A Review on Deep Learning Techniques Applied to Semantic Segmentation
  • 03 - Courses
    • CS224W ~ Jure Leskovec
      • CS224W 2021
      • CS224W 2019
    • Machine Learning ~ Hung-yi Lee
      • ML 2022 Spring
      • ML 2021 Spring
      • ML 2020 Spring
  • 04 - Books
    • Graph
      • Networks ~ Newman
      • Introduction to Graph Theory
    • Graph Neural Networks
      • Graph Representation Learning
      • Graph Neural Networks: Foundations, Frontiers, and Applications
    • Deep Learning
      • Probabilistic Machine Learning An Introduction
      • Probabilistic Machine Learning Advanced Topics
      • Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
    • The Princeton Companion to Mathematics
      • 第Ⅰ部分 引论
      • 第Ⅱ部分 现代数学的起源
      • 第Ⅲ部分 数学概念
      • 第Ⅳ部分 数学的各个分支
      • 第V部分 定理与问题
      • 第VI部分 数学家传记
      • 第VII部分 数学的影响
      • 第VIII部分 卷末的话：一些看法
    • The Riemann Hypothesis
    • Fluent Python
    • Algorithms (Fourth Edition)
  • 05 - Codes
    • OI-WIKI
    • LeetCode
      • 进度表
        • 1、数组
        • 4、动态规划
        • 6、深度优先搜索
        • 7、排序
        • 17、栈
        • 20、回溯
        • 23、链表
        • 26、滑动窗口
      • 单词
    • Pytorch
      • PyTorch-geometric
      • 经典实现
    • TensorFlow
    • Manim
  • 06 - Applications
    • Summary of Recommendation
  • 07 - Others
    • Statistical Learning
      • 信息熵的基本介绍
      • 对偶形式的推导
      • Logistic Regression
      • Support Vector Machine
      • Ensemble Models
      • Mixture Models
      • Restricted Boltzmann Machine
      • [doing] Probabilistic Graphical Models
      • Neural Networks
    • Interesting Videos
      • 3Blue1Brown
      • Veritasium
    • Deep Learning State of the Art (2020) by Lex Fridman
    • Resources
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