Muyun99's wiki 
归档
2022
- 06-03 Structured Knowledge Distillation for Semantic Segmentation
- 05-20 README 美化
- 05-12 常见 Tricks 代码片段
- 05-11 数据增强
- 05-11 使用半精度训练
- 05-11 使用单机多卡分布式训练
- 05-11 PyTorch 常见代码片段
- 04-14 Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
- 03-31 从 Tesla AI Day 看自动驾驶的进展
- 03-18 RepVGG
- 03-16 动态卷积
- 03-16 重参数化宇宙的起源
- 03-15 Decompose to Adapt Domain Disentanglement Faster-RCNN for Cross-domain Object Detection
- 03-08 Data-centric vs Model-centric 的个人拙见
- 01-14 Multi-label 分类中如何计算 mAP
- 01-13 mIoU的计算
- 01-10 General Multi-label Image Classification with Transformers
- 01-07 年少可以听听李宗盛，只是容易上头
- 01-04 GETAM Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation
- 01-04 Do Vision Transformers See Like Convolutional Neural Networks
- 01-04 An Image is Worth 16x16 Words Transformers for Image Recognition at Scale
- 01-04 Unverify
- 01-04 Git 设置代理
2021
- 12-31 2021 年终总结 | 时光飞逝的一年
- 12-24 Clash 配置
- 12-17 服务器重装系统
- 11-03 节点分类是如何训练的
- 11-02 Transformer CAM|Transformer Interpretability Beyond Attention Visualization
- 11-01 Self-Erasing Network for Integral Object Attention
- 11-01 CAM 扩散 | Tell Me Where to Look Guided Attention Inference Network
- 11-01 区域擦除 | Object Region Mining with Adversarial Erasing A Simple Classification to Semantic Segmentation Approach
- 10-24 Awesome-Graph-Neural-Network
- 10-24 Awesome-Knowledge-distillation
- 10-21 自监督系列代码
- 10-16 撰写论文工具
- 10-16 Transformer系列代码
- 10-16 Learning Visual Words for Weakly-Supervised Semantic Segmentation
- 10-14 Awesome weakly supervised semantic segmentation
- 10-14 Awesome-weakly-supervised-semantic-segmentation
- 10-14 DeepLab系列代码
- 10-13 Learning random-walk label propagation for weakly-supervised semantic segmentation
- 10-13 Convolutional Random Walk Networks for Semantic Image Segmentation
- 10-13 Learning Deep Features for Discriminative Localization
- 10-13 Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
- 10-13 NoPeopleAllowed The Three-Step Approach to Weakly Supervised SemanticSegmentation
- 10-13 Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
- 10-13 Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
- 10-12 Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation
- 10-03 Tensorboard 常用用法
- 09-30 计算loss和计算metric
- 09-28 mmseg 推理单张图像并保存
- 09-23 上采样函数
- 09-22 转换cityscapes 到对应的类别
- 09-19 可能会用到的表达
- 09-15 mmseg数据集
- 09-15 Ubuntu系统安装
- 09-14 损失函数的前置知识
- 09-14 矩估计
- 09-14 逻辑回归与sigmoid
- 09-14 softmax与交叉熵
- 09-14 极大似然函数
- 09-13 VALSE Webinar 20-02 元学习与小样本学习
- 09-12 遥感图像建筑物变化检测竞赛学习-1
- 09-10 后期泛谈
- 09-09 胶片相机泛谈
- 09-08 富士相机泛谈
- 09-07 few-shot learning 竞赛学习-2
- 09-07 GPU速度太慢问题排查
- 09-06 Sill-Net Feature Augmentation with Separated Illumination Representation
- 09-06 SCAN Learning to Classify Images without Labels
- 09-06 Improving Unsupervised Image Clustering With Robust Learning
- 09-06 SPICE Semantic Pseudo-labeling for Image Clustering
- 09-05 few-shot learning 竞赛学习
- 09-04 ssh 与 Git 登录
- 09-04 控制
- 09-04 规划
- 09-04 预测
- 09-04 感知
- 09-04 定位
- 09-04 cpp STL 常用用法
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