class-incremental-learning:AANets（CVPR 2021）的PyTorch实施和助记符培训（CVPR 2020）
班级增量学习
文件
用于班级增量学习的自适应聚合网络，CVPR2021。[ ] []
助记符训练：无需忘记的多级增量学习，CVPR2020。[ ] []
引文
如果它们对您的工作有帮助，请引用我们的论文：
@inproceedings { Liu2020AANets ,
author = { Liu, Yaoyao and Schiele, Bernt and Sun, Qianru } ,
title = { Adaptive Aggregation Networks for Class-Incremental Learning } ,
booktitle = { The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) } ,
year = { 20
文件下载
资源详情
[{"title":"（ 73 个子文件 99KB ） class-incremental-learning:AANets（CVPR 2021）的PyTorch实施和助记符培训（CVPR 2020）","children":[{"title":"class-incremental-learning-main","children":[{"title":"adaptive-aggregation-networks","children":[{"title":"utils","children":[{"title":"misc.py <span style='color:#111;'> 4.27KB </span>","children":null,"spread":false},{"title":"gpu_tools.py <span style='color:#111;'> 1.59KB </span>","children":null,"spread":false},{"title":"imagenet","children":[{"title":"utils_dataset.py <span style='color:#111;'> 945B </span>","children":null,"spread":false},{"title":"utils_train.py <span style='color:#111;'> 3.16KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 76B </span>","children":null,"spread":false},{"title":"train_and_eval.py <span style='color:#111;'> 2.71KB </span>","children":null,"spread":false}],"spread":true},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"incremental","children":[{"title":"compute_features.py <span style='color:#111;'> 1.94KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 70B </span>","children":null,"spread":false},{"title":"compute_accuracy.py <span style='color:#111;'> 4.51KB </span>","children":null,"spread":false},{"title":"conv2d_mtl.py <span style='color:#111;'> 4.05KB </span>","children":null,"spread":false}],"spread":true},{"title":"process_fp.py <span style='color:#111;'> 3.08KB </span>","children":null,"spread":false}],"spread":true},{"title":"models","children":[{"title":"modified_resnet_cifar.py <span style='color:#111;'> 4.02KB </span>","children":null,"spread":false},{"title":"modified_resnet.py <span style='color:#111;'> 4.17KB </span>","children":null,"spread":false},{"title":"modified_resnetmtl.py <span style='color:#111;'> 4.29KB </span>","children":null,"spread":false},{"title":"modified_linear.py <span style='color:#111;'> 2.22KB </span>","children":null,"spread":false},{"title":"resnet_cifar.py <span style='color:#111;'> 4.97KB </span>","children":null,"spread":false},{"title":"modified_resnetmtl_cifar.py <span style='color:#111;'> 4.07KB </span>","children":null,"spread":false}],"spread":true},{"title":"main.py <span style='color:#111;'> 5.15KB </span>","children":null,"spread":false},{"title":"trainer","children":[{"title":"base_trainer.py <span style='color:#111;'> 56.69KB </span>","children":null,"spread":false},{"title":"incremental_lucir.py <span style='color:#111;'> 8.00KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"zeroth_phase.py <span style='color:#111;'> 3.66KB </span>","children":null,"spread":false},{"title":"trainer.py <span style='color:#111;'> 10.50KB </span>","children":null,"spread":false},{"title":"incremental_icarl.py <span style='color:#111;'> 5.60KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 7.17KB </span>","children":null,"spread":false}],"spread":true},{"title":"mnemonics-training","children":[{"title":"2_eval","children":[{"title":"utils","children":[{"title":"misc.py <span style='color:#111;'> 3.88KB </span>","children":null,"spread":false},{"title":"gpu_tools.py <span style='color:#111;'> 739B </span>","children":null,"spread":false},{"title":"imagenet","children":[{"title":"utils_dataset.py <span style='color:#111;'> 546B </span>","children":null,"spread":false},{"title":"utils_train.py <span style='color:#111;'> 2.71KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 37B </span>","children":null,"spread":false},{"title":"train_and_eval.py <span style='color:#111;'> 2.43KB </span>","children":null,"spread":false}],"spread":true},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"incremental","children":[{"title":"compute_features.py <span style='color:#111;'> 1.10KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 70B </span>","children":null,"spread":false},{"title":"compute_accuracy.py <span style='color:#111;'> 1.73KB </span>","children":null,"spread":false},{"title":"conv2d_mtl.py <span style='color:#111;'> 3.58KB </span>","children":null,"spread":false},{"title":"compute_confusion_matrix.py <span style='color:#111;'> 1.96KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"models","children":[{"title":"modified_resnet_cifar.py <span style='color:#111;'> 3.65KB </span>","children":null,"spread":false},{"title":"modified_resnet.py <span style='color:#111;'> 3.79KB </span>","children":null,"spread":false},{"title":"modified_resnetmtl.py <span style='color:#111;'> 3.87KB </span>","children":null,"spread":false},{"title":"modified_linear.py <span style='color:#111;'> 2.18KB </span>","children":null,"spread":false},{"title":"resnet_cifar.py <span style='color:#111;'> 4.50KB </span>","children":null,"spread":false},{"title":"modified_resnetmtl_cifar.py <span style='color:#111;'> 3.74KB </span>","children":null,"spread":false}],"spread":true},{"title":"script","children":[{"title":"download_ckpt.sh <span style='color:#111;'> 419B </span>","children":null,"spread":false}],"spread":true},{"title":"main.py <span style='color:#111;'> 4.26KB </span>","children":null,"spread":false},{"title":"trainer","children":[{"title":"train.py <span style='color:#111;'> 12.83KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 724B </span>","children":null,"spread":false},{"title":"run_eval.sh <span style='color:#111;'> 515B </span>","children":null,"spread":false},{"title":"process_imagenet","children":[{"title":"generate_imagenet.py <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"generate_imagenet_subset.py <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 3.16KB </span>","children":null,"spread":false},{"title":"1_train","children":[{"title":"utils","children":[{"title":"misc.py <span style='color:#111;'> 3.82KB </span>","children":null,"spread":false},{"title":"gpu_tools.py <span style='color:#111;'> 764B </span>","children":null,"spread":false},{"title":"compute_features.py <span style='color:#111;'> 1.03KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"compute_accuracy.py <span style='color:#111;'> 4.60KB </span>","children":null,"spread":false},{"title":"conv2d_mtl.py <span style='color:#111;'> 3.58KB </span>","children":null,"spread":false},{"title":"process_mnemonics.py <span style='color:#111;'> 1.59KB </span>","children":null,"spread":false},{"title":"process_fp.py <span style='color:#111;'> 773B </span>","children":null,"spread":false}],"spread":true},{"title":"models","children":[{"title":"modified_resnet_cifar.py <span style='color:#111;'> 3.39KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"modified_linear.py <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false},{"title":"modified_resnetmtl_cifar.py <span style='color:#111;'> 3.96KB </span>","children":null,"spread":false}],"spread":true},{"title":"main.py <span style='color:#111;'> 4.12KB </span>","children":null,"spread":false},{"title":"trainer","children":[{"title":"mnemonics.py <span style='color:#111;'> 29.83KB </span>","children":null,"spread":false},{"title":"baseline.py <span style='color:#111;'> 24.05KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"incremental.py <span style='color:#111;'> 3.38KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.00KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 97B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]
评论信息
其他资源
- Multisim10.0元件库
- IEEE14节点配电网数据
- OpenOCD_GUI.7z
- .net版本非常好看的留言板源码（Metro风格），附完整数据库与源码，可以直接使用
- 基于Java-Web的在线点餐系统
- 真实用户头像（网站、APP、社交网站)可用7000
- 基于PCA人脸识别Matlab代码(毕设必备)
- java课程设计 大二时候的课程设计 老师评定为优的很实用
- VisualSFM+MeshLab三维重建
- Tiva TM4C123FH6PM中文数据手册
- ISO GUIDE 33：2015 标准物质/样品-使用标准物质/样品的良好做法 - 完整英文版（37页）
- On achieving maximum streaming rate in hybrid wired/wireless overlay networks
- 有道云笔记网页封装版（Ubuntu）
- win32DiskImager
- nexus-3.6.1-02-unix.tar.gz
- JAVA JSP+MYSQL在线教学系统 源代码 论文
- Android 视频直播Demo
- java毕设之网上书店
- 008神器永久破解版
- Sim_EKB_Install_2018_10_30
- CSerialPort串口类2014-12-18版Unicode版
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明