awesome-ai-awesomeness/README.md

# Awesome AI Awesomeness

A curated list of awesome awesomeness about artificial intelligence(AI).

If you want to contribute to this list (please do), send me a pull request.

# Table of Contents

<!-- MarkdownTOC depth=4 -->

- [Artificial Intelligence(AI)](#AI)
- [Machine Learning(ML)](#ML)
- [Deep Learning(DL)](#DL)
- [Computer Vision(CV)](#CV)
- [Natural Language Processing(NLP)](#NLP)
- [Speech Recognition](#SR)
- [Other Research Topics](#ORT)
- [Programming Languages](#PL)
- [Framework](#Framework)
- [Datasets](#Datasets)

<a name="AI"></a>

# Artificial Intelligence(AI)

- [AI](https://github.com/owainlewis/awesome-artificial-intelligence)
- [AI-Use-Cases](https://github.com/faktionai/awesome-ai-usecases)
- [AI residency programs information](https://github.com/ankitshah009/all-about-ai-residency)

<a name="ML"></a>

# Machine Learning(ML)

- [ML](https://github.com/josephmisiti/awesome-machine-learning)
- [ML-Source-Code](https://github.com/src-d/awesome-machine-learning-on-source-code)
- [ML-CN](https://github.com/jobbole/awesome-machine-learning-cn)
- [Adversarial-ML](https://github.com/yenchenlin/awesome-adversarial-machine-learning)
- [Quantum-ML](https://github.com/krishnakumarsekar/awesome-quantum-machine-learning)
- [3D-Machine-Learning](https://github.com/timzhang642/3D-Machine-Learning)
- [Machine Learning Interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability)
- [Machine Learning System](https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning)
- [Mobile Machine Learning](https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning)
- [Machine Learning Problems](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems)
- [Gradient Boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers)
- [Decision Tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)

<a name="DL"></a>

# Deep Learning(DL)

- [DL](https://github.com/ChristosChristofidis/awesome-deep-learning)
- [DL-Papers](https://github.com/terryum/awesome-deep-learning-papers)
- [DL-Resources](https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources)
- [DeepLearning-500-questions](https://github.com/scutan90/DeepLearning-500-questions)
- [Deep-Learning-in-Production](https://github.com/ahkarami/Deep-Learning-in-Production)
- [DNN Compression and Acceleration](https://github.com/MingSun-Tse/EfficientDNNs)
- [Architecture Search](https://github.com/markdtw/awesome-architecture-search)
- [Deep Learning for Graphs](https://github.com/DeepGraphLearning/LiteratureDL4Graph)
- [Real-time Network](https://github.com/wpf535236337/real-time-network)
- [Deep Learning Interpretability](https://github.com/oneTaken/awesome_deep_learning_interpretability)

<a name="CV"></a>

# Computer Vision(CV)

- [CV](https://github.com/jbhuang0604/awesome-computer-vision)
- [CV2](https://github.com/kjw0612/awesome-deep-vision)
- [CV-People](Awesome-People-in-Computer-Vision)
- [DeepFakes](https://github.com/datamllab/awesome-deepfakes-materials)
- [Event-based Vision Resources](https://github.com/uzh-rpg/event-based_vision_resources)
- Research Topics
    - [Action Recognition](https://github.com/jinwchoi/awesome-action-recognition)
    - [Colorization](https://github.com/MarkMoHR/Awesome-Image-Colorization)
    - [Image Classification](https://github.com/weiaicunzai/awesome-image-classification)
        - [imgclsmob](https://github.com/osmr/imgclsmob)
    - [Object Detection](https://github.com/amusi/awesome-object-detection)
    - [Video Object Detection](https://github.com/huanglianghua/video-detection-paper-list)
    - Face
        - [Face Detection & Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition)
        - [awesome-face](https://github.com/polarisZhao/awesome-face)
        - [Facial Expression Recognition (FER)](https://github.com/EvelynFan/AWESOME-FER)
        - [Face Landmark Detection](https://github.com/mrgloom/Face-landmarks-detection-benchmark)
        - [Landmark Detection](https://github.com/D-X-Y/landmark-detection)
    - [Gaze Estimation](https://github.com/WuZhuoran/awesome-gaze)
    - Image Segmentation
        - [Semantic Segmentation](https://github.com/mrgloom/awesome-semantic-segmentation)
        - [Segmentation.X](https://github.com/wutianyiRosun/Segmentation.X)
        - [Panoptic Segmentation](https://github.com/Angzz/awesome-panoptic-segmentation)
        - [Weakly Supervised Semantic Segmentation](https://github.com/JackieZhangdx/WeakSupervisedSegmentationList)
        - [Referring Image Segmentation](https://github.com/MarkMoHR/Awesome-Referring-Image-Segmentation)
    - [Object Tracking](https://github.com/foolwood/benchmark_results)
        - [Visual Tracking1](https://github.com/foolwood/benchmark_results)
        - [Visual Tracking2](https://github.com/czla/daily-paper-visual-tracking)
        - [Multi-Object Tracking](https://github.com/SpyderXu/multi-object-tracking-paper-list)
        - [Tracking and Detection](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection)
    - [Pose estimation](https://github.com/wjbKimberly/pose_estimation_CVPR_ECCV_2018)
        - Human Pose estimation
          - [Human Pose estimation 1](https://github.com/cbsudux/awesome-human-pose-estimation)
          - [Human Pose estimation 2](https://github.com/wangzheallen/awesome-human-pose-estimation)
        - [Hand Pose estimation](https://github.com/xinghaochen/awesome-hand-pose-estimation)
    - [Human Motion](https://github.com/derikon/awesome-human-motion)
    - Scene Text
        - [Scene Text Localization and Recognition](https://github.com/chongyangtao/Awesome-Scene-Text-Recognition)
        - [Scene Text Localization & Recognition Resources](https://github.com/whitelok/image-text-localization-recognition)
        - [Scene Text Detection and Recognition](https://github.com/Jyouhou/SceneTextPapers)
        - [Text Detection and Recognition](https://github.com/hwalsuklee/awesome-deep-text-detection-recognition)
        - [Scene Text Recognition Resources](https://github.com/HCIILAB/Scene-Text-Recognition)
    - Super Resolution
        - [Super Resolution (ChaofWang)](https://github.com/ChaofWang/Awesome-Super-Resolution)
        - [Super Resolution (ptkin)](<https://github.com/ptkin/Awesome-Super-Resolution>)
        - [Image Super Resolution](https://github.com/YapengTian/Single-Image-Super-Resolution)
        - [Video Super Resolution](https://github.com/LoSealL/VideoSuperResolution)
    - 3D
        - [3D Reconstruction](https://github.com/openMVG/awesome_3DReconstruction_list) 
    - [OCR](https://github.com/kba/awesome-ocr)
    - Re-ID
        - [Person Re-ID(1)](https://github.com/bismex/Awesome-person-re-identification)
        - [Person Re-ID(2)](https://github.com/FDU-VTS/Awesome-Person-Re-Identification)
        - [Vehicle Re-ID](https://github.com/knwng/awesome-vehicle-re-identification)
    - [Pedestrian Attribute Recognition](https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List)
    - [Image Captioning](https://github.com/zhjohnchan/awesome-image-captioning)
    - [Question Answering](https://github.com/dapurv5/awesome-question-answering)
    - [Crowd Counting](https://github.com/gjy3035/Awesome-Crowd-Counting)
    - [Lane Detection](https://github.com/amusi/awesome-lane-detection)
    - [Low Lignt Enhancement](https://github.com/Elin24/Awesome-Low-Lignt-Enhancement) 
    - Image Retrieval
        - [Awesome image retrieval papers (1)](https://github.com/willard-yuan/awesome-cbir-papers)
        - [Awesome image retrieval papers (2)](https://github.com/lgbwust/awesome-image-retrieval-papers)
    - [Medical Imaging](https://github.com/fepegar/awesome-medical-imaging)
        - [Medical Data](https://github.com/beamandrew/medical-data)
        - [Medical imaging datasets](https://github.com/sfikas/medical-imaging-datasets)
        - [Awesome GAN for Medical Imaging](https://github.com/xinario/awesome-gan-for-medical-imaging)
        - [Deep Learning for Medical Applications](https://github.com/albarqouni/Deep-Learning-for-Medical-Applications)
    - [Image Inpainting](https://github.com/1900zyh/Awesome-Image-Inpainting)
    - [Image Dehazing](https://github.com/youngguncho/awesome-dehazing)
    - Image Denoising
        - [reproducible-image-denoising-state-of-the-art](https://github.com/wenbihan/reproducible-image-denoising-state-of-the-art)
        - [Image-Denoising-State-of-the-art](https://github.com/flyywh/Image-Denoising-State-of-the-art)
        - [Image and Video Denoising](https://github.com/z-bingo/awesome-image-denoising-state-of-the-art)
    - [Image Deraining](https://github.com/nnUyi/DerainZoo)
    - [Image/Video Deblurring]( https://github.com/subeeshvasu/Awesome-Deblurring )
    - Image to Image(img2img)
        - [lzhbrian/Image to Image](https://github.com/lzhbrian/image-to-image-papers)
        - [xiaweihao/Image to Image](https://github.com/xiaweihao/awesome-image-translation)
    - [Video Analysis](https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis)
    - [Video Object Segmentation(VOS)](https://github.com/du0915/Video-Object-Segmentation-Paper-List)
    - [Edge Detection](<https://github.com/MarkMoHR/Awesome-Edge-Detection-Papers>)
    - [Local and Global Descriptor](https://github.com/shamangary/awesome-local-global-descriptor)
    - Salience
        - [Salient Object Detection(SOD)](https://github.com/jiwei0921/SOD-CNNs-based-code-summary-)
        - [Saliency Detection & Segmentation](https://github.com/lartpang/awesome-segmentation-saliency-dataset#another-awesome-dataset-list)
    - [Fashion + AI](https://github.com/lzhbrian/Cool-Fashion-Papers)
    - [Event-based Vision Resources](https://github.com/uzh-rpg/event-based_vision_resources)

<a name="NLP"></a>

# Natural Language Processing(NLP)

- [NLP](https://github.com/keon/awesome-nlp)
- [NLP-progress](https://github.com/sebastianruder/NLP-progress)
- [CoreNLP](https://github.com/stanfordnlp/CoreNLP)
- [NLPIR](https://github.com/NLPIR-team/NLPIR)
- [nlp_course](https://github.com/yandexdataschool/nlp_course)
- [nlp-datasets](https://github.com/niderhoff/nlp-datasets)
- [nlp-reading-group](https://github.com/clulab/nlp-reading-group)
- [Awesome-Chinese-NLP](https://github.com/crownpku/Awesome-Chinese-NLP): 中文自然语言处理相关资料
- [awesome-dl4nlp](https://github.com/brianspiering/awesome-dl4nlp)
- [awesome-sentence-embedding](https://github.com/Separius/awesome-sentence-embedding)

<a name="SR"></a>

# Speech Recognition

- [speech_recognition](https://github.com/Uberi/speech_recognition)
- [awesome-speech-recognition-speech-synthesis-papers](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers)

<a name="ORT"></a>

# Other Research Topics

- Bayesian
     - [Bayesian](https://github.com/dimenwarper/awesome-bayes)
     - [Deep Bayesian](https://github.com/otokonoko8/deep-Bayesian-nonparametrics-papers)
- [Capsule Networks](https://github.com/sekwiatkowski/awesome-capsule-networks)
- [Data Augmentation](https://github.com/AgaMiko/data-augmentation-review)
- GAN
  - [really-awesome-gan](https://github.com/nightrome/really-awesome-gan)
  - [AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers)
  - [the-gan-zoo](https://github.com/hindupuravinash/the-gan-zoo)
  - [Keras-GAN](https://github.com/eriklindernoren/Keras-GAN)
  - [gans-awesome-applications](https://github.com/nashory/gans-awesome-applications): Curated list of awesome GAN applications and demo
- [SLAM](https://github.com/kanster/awesome-slam)
  - [SLAM List](https://github.com/OpenSLAM/awesome-SLAM-list)
  - [VSLAM](https://github.com/tzutalin/awesome-visual-slam)
  - [SLAM(Chinese)](https://github.com/YiChenCityU/Recent_SLAM_Research)
  - [SLAM Datasets](https://github.com/youngguncho/awesome-slam-datasets)
  - [SFM-Visual-SLAM](https://github.com/marknabil/SFM-Visual-SLAM)
  - [SLAM Resources](https://github.com/ckddls1321/SLAM_Resources)
- [Graph Neural Networks(GNN)](https://github.com/thunlp/GNNPapers)
- [Reinforcement Learning](https://github.com/aikorea/awesome-rl)
     - [Implementation of Reinforcement Learning Algorithms](https://github.com/dennybritz/reinforcement-learning)
     - [Reinforcement Learning Chinese](https://github.com/wwxFromTju/awesome-reinforcement-learning-zh)：中文整理的强化学习资料
- [Transfer Learning](https://github.com/jindongwang/transferlearning)
- [Trajectory Prediction](https://github.com/jiachenli94/Awesome-Interaction-aware-Trajectory-Prediction)
- [Zero-Shot Learning](https://github.com/chichilicious/awesome-zero-shot-learning)
- Few-Shot Learning
     - [Duan-JM/Few-Shot Learning](https://github.com/Duan-JM/awesome-papers-fewshot)
     - [e-271/Few-Shot Learning](https://github.com/e-271/awesome-few-shot-learning)
- [Federated Learning](https://github.com/poga/awesome-federated-learning)
- Meta-Learning
     - [Meta-Learning1](https://github.com/dragen1860/awesome-meta-learning)
     - [Meta-Learning2](https://github.com/sudharsan13296/Awesome-Meta-Learning)
- [Self-Supervised](https://github.com/jason718/awesome-self-supervised-learning)
- [Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification)
- [Incremental Learning](https://github.com/xialeiliu/Awesome-Incremental-Learning)
- [AutoML](https://github.com/hibayesian/awesome-automl-papers)
     - [AutoML Survey](https://github.com/DataSystemsGroupUT/AutoML_Survey)
     - [AutoML-and-Lightweight-Models](https://github.com/guan-yuan/awesome-AutoML-and-Lightweight-Models)
     - [NAS](https://github.com/D-X-Y/Awesome-NAS)
     - [Architecture Search](https://github.com/markdtw/awesome-architecture-search)
     - [LITERATURE ON NEURAL ARCHITECTURE SEARCH](https://www.automl.org/automl/literature-on-neural-architecture-search/)
- [Model Compression](https://github.com/cedrickchee/awesome-ml-model-compression)
     - [EfficientDNNs](https://github.com/MingSun-Tse/EfficientDNNs)
     - [Model Compression and Acceleration](https://github.com/memoiry/Awesome-model-compression-and-acceleration)
     - [Neural Network Pruning](https://github.com/he-y/Awesome-Pruning)
-  [Multimodal Research](https://github.com/Eurus-Holmes/Awesome-Multimodal-Research)
-  [Multimodal Machine Learning](https://github.com/pliang279/awesome-multimodal-ml)
- [Domain Adaptation](https://github.com/zhaoxin94/awsome-domain-adaptation)
- [Robotics](https://github.com/kiloreux/awesome-robotics)
- [Recommender Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems)
- Autonomous Vehicles
     - [Autonomous Vehicles](https://github.com/takeitallsource/awesome-autonomous-vehicles)
     - [Autonomous Vehicles-CH]( https://github.com/DeepTecher/awesome-autonomous-vehicle )
     - [Lidar Point cloud processing for Autonomous Driving](https://github.com/beedotkiran/Lidar_For_AD_references)
- [Anomaly Detection](https://github.com/yzhao062/anomaly-detection-resources)
- [Point Cloud Analysis](https://github.com/Yochengliu/awesome-point-cloud-analysis)
- [3D Point Clouds](https://github.com/QingyongHu/SoTA-Point-Cloud)
- [Affective_Computing](https://github.com/suzana-ilic/Deep_Learning_Affective_Computing)
- Knowledge Distillation
     - [Knowledge Distillation(dkozlov)](https://github.com/dkozlov/awesome-knowledge-distillation)
     - [Knowledge Distillation(FLHonker)](https://github.com/FLHonker/Awesome-Knowledge-Distillation)
- [Click-Through Rate Prediction](https://github.com/shenweichen/DeepCTR)
- [VAE](https://github.com/matthewvowels1/Awesome-VAEs)

<a name="PL"></a>

# Programming Languages

- [C](https://notabug.org/koz.ross/awesome-c)
- [C++](https://github.com/fffaraz/awesome-cpp)
- [Python](https://github.com/vinta/awesome-python)
- [JAVA](https://github.com/akullpp/awesome-java)
- [JavaScript](awesome-javascript)
- [Julia](https://github.com/svaksha/Julia.jl)
- [MATLAB](https://github.com/uhub/awesome-matlab)
- [R](https://github.com/qinwf/awesome-R)

<a name="Framework"></a>
# Framework

- [TensorFlow](https://github.com/jtoy/awesome-tensorflow)
  - [TensorFlow From Zero To- One](https://github.com/amusi/TensorFlow-From-Zero-To-One)
  - [TensorFlow Lite](https://github.com/margaretmz/awesome-tflite)
- [PyTorch](https://github.com/bharathgs/Awesome-pytorch-list)
  - [PyTorch From Zero To- One](https://github.com/amusi/PyTorch-From-Zero-To-One)
- [Keras](https://github.com/fchollet/keras-resources)
- [MXNet](https://github.com/chinakook/Awesome-MXNet)
- [Caffe](https://github.com/MichaelXin/Awesome-Caffe)
- [Torch](https://github.com/carpedm20/awesome-torch)
- [Chainer](awesome-chainer)

<a name="Datasets"></a>

# Datasets

- [Segmentation & Saliency detection](https://github.com/lartpang/awesome-segmentation-saliency-dataset)