堆叠去噪自编码器matlab代码-Computer-Vision-Action:计算机视觉学习，包括python机器学习动作；基...

# Computer Vision Task Collect ## main computer vision task - reading paper : [CV-arXiv-Daily](https://github.com/zhengzhugithub/CV-arXiv-Daily) - [zhengzhugithub/AwesomeComputerVision](https://github.com/zhengzhugithub/AwesomeComputerVision) - [handong1587.github.io](https://handong1587.github.io/index.html) ### low level - [wenbihan/reproducible-image-denoising-state-of-the-art](https://github.com/wenbihan/reproducible-image-denoising-state-of-the-art) - [YapengTian/Single-Image-Super-Resolution](https://github.com/YapengTian/Single-Image-Super-Resolution) - [LoSealL/VideoSuperResolution](https://github.com/LoSealL/VideoSuperResolution) ### high level - [junyanz/CatPapers](https://github.com/junyanz/CatPapers) - [TerenceCYJ/3D-Hand-Pose-Estimation-Papers](https://github.com/TerenceCYJ/3D-Hand-Pose-Estimation-Papers) - [wangzheallen/awesome-human-pose-estimation](https://github.com/wangzheallen/awesome-human-pose-estimation) - [visual tracker benchmark results](https://github.com/foolwood/benchmark_results) - [gjy3035/Awesome-Crowd-Counting](https://github.com/gjy3035/Awesome-Crowd-Counting) - [https://github.com/ChanChiChoi/awesome-Face_Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) ### GAN and Text - [zhangqianhui/AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers) - [ lzhbrian/image-to-image-papers](https://github.com/lzhbrian/image-to-image-papers) - [Jyouhou/SceneTextPapers](https://github.com/Jyouhou/SceneTextPapers) - [chongyangtao/Awesome-Scene-Text-Recognition](https://github.com/chongyangtao/Awesome-Scene-Text-Recognition) - [wanghaisheng/awesome-ocr](https://github.com/wanghaisheng/awesome-ocr) - [ChenChengKuan/awesome-text-generation](https://github.com/ChenChengKuan/awesome-text-generation) ### other - [cjmcv/deeplearning-paper-notes](https://github.com/cjmcv/deeplearning-paper-notes) - [floodsung/Deep-Learning-Papers-Reading-Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) - [kjw0612/awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision) - [ sun254/awesome-model-compression-and-acceleration](https://github.com/sun254/awesome-model-compression-and-acceleration) - [chester256/Model-Compression-Papers](https://github.com/chester256/Model-Compression-Papers) - [ dragen1860/awesome-AutoML](https://github.com/dragen1860/awesome-AutoML) - [markdtw/awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search) - [kjw0612/awesome-rnn](https://github.com/kjw0612/awesome-rnn) - [DeepTecher/AutonomousVehiclePaper](https://github.com/DeepTecher/AutonomousVehiclePaper) ## object detection - [amusi/awesome-object-detection](https://github.com/amusi/awesome-object-detection) - [hoya012/deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) - [DetectionTeamUCAS-TF](https://github.com/DetectionTeamUCAS) - [facebookresearch/maskrcnn-benchmark-pytorch](https://github.com/facebookresearch/maskrcnn-benchmark) - [roytseng-tw/Detectron.pytorch](https://github.com/roytseng-tw/Detectron.pytorch) ## image retrieval/search/Re-Id - [willard-yuan/awesome-cbir-papers](https://github.com/willard-yuan/awesome-cbir-papers) - [filipradenovic/cnnimageretrieval-pytorch](https://github.com/filipradenovic/cnnimageretrieval-pytorch) - [Cysu/open-reid](https://github.com/Cysu/open-reid) - [layumi/Person_reID_baseline_PyTorch](https://github.com/layumi/Person_reID_baseline_pytorch) - [michuanhaohao/reid-strong-baseline](https://github.com/michuanhaohao/reid-strong-baseline) ## segmentation - [GeorgeSeif/Semantic-Segmentation-Suite-TF](https://github.com/GeorgeSeif/Semantic-Segmentation-Suite) - [ansleliu/LightNet-PyTorch](https://github.com/ansleliu/LightNet) - [meetshah1995/pytorch-semseg](https://github.com/meetshah1995/pytorch-semseg) - [ speedinghzl/pytorch-segmentation-toolbox](https://github.com/speedinghzl/pytorch-segmentation-toolbox) - [mrgloom/awesome-semantic-segmentation](https://github.com/mrgloom/awesome-semantic-segmentation) - [Semantic Segmentation](https://www.aiuai.cn/aifarm62.html) - [Semantic Segmentation论文整理](https://zhangbin0917.github.io/2018/09/18/Semantic-Segmentation/) ## conference - [2018-2019 International Conferences](https://github.com/JackieTseng/conference_call_for_paper) ## dataset - [遥感数据集](https://zhangbin0917.github.io/2018/06/12/%E9%81%A5%E6%84%9F%E6%95%B0%E6%8D%AE%E9%9B%86/) - [D-X-Y/awesome-NAS](https://github.com/D-X-Y/awesome-NAS) - [瑕疵检测：Defects Inspection](https://github.com/sundyCoder/DEye) / [阿里天池铝表面瑕疵检测](https://tianchi.aliyun.com/competition/entrance/231682/information) # Kaggle-Action - [iphysresearch/DataSciComp: Active Competitons to Join ](https://github.com/iphysresearch/DataSciComp) - [geekinglcq/CDCS](https://github.com/geekinglcq/CDCS) :Chinese Data Competitions' Solutions - [Data-Competition-TopSolution](https://github.com/Smilexuhc/Data-Competition-TopSolution) # Computer Vision Study - [AI算法工程师](http://www.huaxiaozhuan.com/) - [LeetCode](https://github.com/ranjiewwen?q=leetcode&tab=stars&utf8=%E2%9C%93&utf8=%E2%9C%93&q=leetcode) ## python learning - [python machine learning in mooc](http://www.icourse163.org/course/BIT-1001872001) ## image processing - Opencv - Vlfeat ## machine learning - Sklearn - Machine Learning in Action：Read machine learning and analyze code implementation. ## deeping learning - [ChristosChristofidis/awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning) - [deepleraning.ai-course](https://github.com/ranjiewwen/Computer-Vision-Action/tree/master/deeplearning.ai%20course) ：Neural Networks and Deep Learning，Improving Deep Neural Networks，Convolutional Neural Network. ## computer vision based on deep learning - Xiaoxiang College course study notes, PPT and resources are very detailed. - [CS231n](https://github.com/cthorey/CS231):Convolutional Neural Networks for Visual Recognition; - [斯坦福CS231n学习笔记-中文系列](https://www.zybuluo.com/hanxiaoyang/note/442846) - [CS224n](https://github.com/hankcs/CS224n):Natural Language Processing with Deep Learning ## deep learning framwork - [Tensorflow-Project-Template](https://github.com/MrGemy95/Tensorflow-Project-Template) - [SpikeKing/DL-Project-Template](https://github.com/SpikeKing/DL-Project-Template) - [victoresque/pytorch-template](https://github.com/victoresque/pytorch-template) - [tiny-dnn](https://github.com/ranjiewwen/tiny-dnn) > header only, dependency-free deep learning framework in C++11 - [DeepLearnToolbox](https://github.com/DIP-ML-AI/DeepLearnToolbox) > Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started. - MatConvNet - [TVM](https://github.com/dmlc/tvm) - [如何评价陈天奇的模块化深度学习系统NNVM？](https://www.zhihu.com/question/51216952) - [深度学习编译中间件之NNVM](https://blog.csdn.net/sanallen/article/category/7429137)