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diff --git a/2Dto3D.md b/2Dto3D.md
@@ -4,9 +4,81 @@ https://github.com/tensorflow/models/tree/master/research/struct2depth
 DeepTAM: Deep Tracking and Mapping
 https://github.com/lmb-freiburg/deeptam
 
+FADNet: A Fast and Accurate Network for Disparity Estimation
+https://github.com/HKBU-HPML/FADNet
+
+code for Mesh R-CNN, an academic publication, presented at ICCV 2019
+https://github.com/facebookresearch/meshrcnn
+
+A PyTorch Library for Accelerating 3D Deep Learning Research
+https://github.com/NVIDIAGameWorks/kaolin
+
+A pytorch implementation of "D4LCN: Learning Depth-Guided Convolutions for Monocular 3D Object Detection"
+https://github.com/dingmyu/D4LCN
+
+it referenced paper of GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB.
+https://github.com/Ninebell/GaneratedHandsForReal_TIME
+
+Hierarchical Deep Stereo Matching on High Resolution Images, CVPR 2019. 
+https://github.com/gengshan-y/high-res-stereo
+
+Learning View Priors for Single-view 3D Reconstruction
+https://github.com/hiroharu-kato/view_prior_learning
+
+The official implementation of the ICCV 2019 paper "GraphX-convolution for point cloud deformation in 2D-to-3D conversion".
+https://github.com/justanhduc/graphx-conv
+
+Official pytorch implementation of "Indoor Depth Completion with Boundary Consistency and Self-Attention. Huang et al. RLQ@ICCV 2019." https://arxiv.org/abs/1908.08344
+https://github.com/patrickwu2/Depth-Completion
+
+MVSNet: Depth Inference for Unstructured Multi-view Stereo. 
+https://github.com/xy-guo/MVSNet_pytorch
+
+Neural network code for Deep Blending for Free-Viewpoint Image-Based Rendering (SIGGRAPH Asia 2018)
+https://github.com/Phog/DeepBlending
+
+Extreme View Synthesis
+https://github.com/NVlabs/extreme-view-synth
+
+Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (NeurIPS 2019)
+https://github.com/nv-tlabs/DIB-R
+
+CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
+https://github.com/yan99033/CNN-SVO
+
+TriDepth: Triangular Patch-based Deep Depth Prediction [Kaneko+, ICCVW2019(oral)]
+https://github.com/syinari0123/tridepth
+
+From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
+https://github.com/cogaplex-bts/bts
+
+Geometry meets semantics for semi-supervised monocular depth estimation - ACCV 2018
+https://github.com/CVLAB-Unibo/Semantic-Mono-Depth
+
+PyTorch implementation for LayoutNet v2 in the paper: "3D Manhattan Room Layout Reconstruction from a Single 360 Image"
+https://github.com/zouchuhang/LayoutNetv2
+
+Real-Time 3D Semantic Reconstruction from 2D data
+https://github.com/MIT-SPARK/Kimera-Semantics
+
+This repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf
+https://github.com/VCL3D/DeepDepthDenoising
+
+This is the project page of the paper "Flow-Motion and Depth Network for Monocular Stereo and Beyond''
+https://github.com/HKUST-Aerial-Robotics/Flow-Motion-Depth
+
+【神经网络3D重建资源列表】
+https://github.com/natowi/3D-Reconstruction-with-Neural-Network
+
+Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video (NeurIPS 2019)
+https://github.com/JiawangBian/SC-SfMLearner-Release
+
 深度学习深度估计研究指南
 https://pan.baidu.com/s/1RhORsmInOk1ZEmOKuUeybw
 
+《Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints》
+https://www.arxiv-vanity.com/papers/2001.02606/
+
 (PyTorch)合成-现实(Synthetic-to-Realistic)转换深度估计
 https://github.com/lyndonzheng/Synthetic2Realistic
 

diff --git a/AR.md b/AR.md
@@ -16,6 +16,9 @@ https://www.aminer.cn/research_report/5cf7993c00eea1f1d521d784
 基于ARKit3的人体实时分割与打码 via:Kitasenju Design 
 https://weibo.com/tv/v/HApWK8FAc?fid=1034:4386795211940756
 
+C++ implementation of Fast Depth Densification for Occlusion-aware Augmented Reality (SIGGRAPH-Asia 2018)
+https://github.com/muskie82/AR-Depth-cpp
+
 用 普通手机 + Google Street View App + Google Tour Creator 可以轻松创建很酷的VR导览 Demo
 
 很有感觉的未来派#AR# 

diff --git a/Algorithm.md b/Algorithm.md
@@ -16,9 +16,40 @@ https://mp.weixin.qq.com/s/fIRW0z-AMwFwsvC8hvYnYg
 【旅行商问题解决算法可视化】’Traveling Salesman Problem - Visualization of algorithms for solving the traveling salesman problem'
 https://github.com/jhackshaw/tspvis
 
+【Python字符串相似性算法库】
+https://github.com/luozhouyang/python-string-similarity
+
+'面试算法/数据结构笔记 - Algorithm for Interview（面试算法笔记-中文）
+https://github.com/imhuay/Algorithm_for_Interview-Chinese
+
+'信息学竞赛讲课课件'
+https://github.com/huzecong/oi-slides
+本科毕业于清华大学，现于CMU攻读硕士的胡泽聪同学，在GitHub上放出了他从2013年至2018年的所有信息学竞赛讲课课件。主要内容包括：TopCoder题目选讲、CodeChef题目选讲、网络流模型、NOIP图论、NOIP数学方法、数据结构及应用、动态规划、线性代数、概率与期望等。
+
+'LeetCode-book - leetcode 1~400知识点&题型总结&leetcode对应题表' 
+https://github.com/huxiaoman7/leetcodebook
+
+动态规划之博弈问题
+https://mp.weixin.qq.com/s/gY9aHcIaBz2iPQraptJmgQ
+
+【基于Anki的算法与数据结构面试准备资料集】
+https://github.com/teivah/algodeck
+
+《详解一道高频面试题：接雨水》
+https://mp.weixin.qq.com/s/sO3rokhr_xsjZ0wXf6j5GQ
+
 算法和编程面试题精选TOP50！(附代码+解题思路+答案)
 https://mp.weixin.qq.com/s/ABSgO36dGLa1_d5daN2c8A
 
+【算法分类知识框架】
+https://static.coggle.it/diagram/WHeBqDIrJRk-kDDY/t/categories-of-algorithms-non-exhaustive
+
+《程序员修炼之路》作者将自己作为一个过来人，总结了自己程序员修炼之路的经验，以及给后辈的一些建议，分享给初入门的程序员同学。
+https://github.com/Snailclimb/programmer-advancement
+
+107+ 条最常出现的编码面试问题及详细解决方案
+https://github.com/hoanhan101/algo
+
 【Leetcode 的 Python 参考方案/解析】’ Leetcode Python Solution and Explanation. Also a Guide to Prepare for Software Engineer Interview.'
 https://github.com/wuduhren/leetcode-python
 

diff --git a/AnomalyDetection.md b/AnomalyDetection.md
@@ -10,6 +10,30 @@ https://www.amazon.com/Beginning-Anomaly-Detection-Python-Based-Learning/dp/1484
 深度学习缺陷检测
 https://github.com/sundyCoder/DEye
 
+Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
+https://github.com/maxkferg/metal-defect-detection
+
+A Benchmark for Anomaly Segmentation
+https://github.com/hendrycks/anomaly-seg
+
+Code for the CVPR'19 paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos"
+https://github.com/RomeroBarata/skeleton_based_anomaly_detection
+
+### Repository for the One class neural networks paper
+https://github.com/raghavchalapathy/oc-nn
+
+An Acceleration System for Large-Scale Unsupervised Anomaly Detection
+https://github.com/yzhao062/SUOD
+
+### （最先进的缺陷检测网络） A Tensorflow implementation of "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection"
+https://github.com/Wslsdx/Deep-Learning-Approach-for-Surface-Defect-Detection
+
+Cork/Face Presentation Attack Detection
+https://github.com/ee09115/spoofing_detection
+
+### 【深度学习混凝土结构裂纹检测】
+https://github.com/priya-dwivedi/Deep-Learning/blob/master/crack_detection/Crack%20Detection%20Model.ipynb
+
 A PyTorch implementation of the Deep SVDD anomaly detection method
 https://github.com/lukasruff/Deep-SVDD-PyTorch
 

diff --git a/Audio.md b/Audio.md
@@ -103,6 +103,60 @@ https://github.com/deezer/spleeter
 【开源语音分离/增强库】
 https://github.com/speechLabBcCuny/onssen
 
+Feature extractor for DL speech processing.
+https://github.com/bepierre/SpeechVGG
+
+Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data
+https://github.com/KunZhou9646/Nonparallel-emotional-VC
+
+This is a PyTorch re-implementation of Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition.
+https://github.com/foamliu/Speech-Transformer
+
+【Athena：开源端到端语音识别引擎】
+https://github.com/athena-team/athena
+
+PREDICTING EXPRESSIVE SPEAKING STYLE FROM TEXT IN END-TO-END SPEECH SYNTHESIS
+https://github.com/Yangyangii/TPGST-Tacotron
+
+PyTorch implementation of LF-MMI for End-to-end ASR
+https://github.com/YiwenShaoStephen/pychain
+
+Audio samples from ICML2019 "Almost Unsupervised Text to Speech and Automatic Speech Recognition"
+https://github.com/RayeRen/unsuper_tts_asr
+
+ Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.
+ https://github.com/CSTR-Edinburgh/ophelia
+
+Efficient neural speech synthesis
+https://github.com/MlWoo/LPCNet
+
+Code for Vision-Infused Deep Audio Inpainting (ICCV 2019)
+https://github.com/Hangz-nju-cuhk/Vision-Infused-Audio-Inpainter-VIAI
+
+deep learning based speech enhancement using keras or pytorch
+https://github.com/yongxuUSTC/sednn
+
+Multi-voice singing voice synthesis
+https://github.com/MTG/WGANSing
+
+【用涂鸦“唱歌”：将图像合成为声音】
+https://github.com/jeonghopark/SketchSynth-Simple
+
+【面向语音识别的中文/英文发音辞典】’
+https://github.com/speech-io/BigCiDian
+
+### 【Kaldi/TensorFlow实现的神经网络说话人验证系统】
+https://github.com/someonefighting/tf-kaldi-speaker-master
+
+### Facebook开源低延迟在线语音识别框架wav2letter
+https://github.com/facebookresearch/wav2letter/wiki/Inference-Framework
+
+【GridSound：在线数字音频编辑器】
+https://github.com/GridSound/daw
+
+【Asteroid：基于PyTorch的音源分离工具集】
+https://github.com/mpariente/ASSteroid
+
 【MelGAN 超快音频合成】
 https://github.com/descriptinc/melgan-neurips
 
@@ -112,6 +166,9 @@ https://github.com/haryoa/note_music_generator
 音频分析/音乐检索相关数据集大列表
 https://www.audiocontentanalysis.org/data-sets/
 
+### 【用TensorRT在GPU上部署实时文本-语音合成应用】
+https://devblogs.nvidia.com/how-to-deploy-real-time-text-to-speech-applications-on-gpus-using-tensorrt/
+
 用WaveNet让语音受损用户重拾原声(少样本自适应自然语音合成)
 https://deepmind.com/blog/article/Using-WaveNet-technology-to-reunite-speech-impaired-users-with-their-original-voices
 

diff --git a/C&C++.md b/C&C++.md
@@ -7,6 +7,9 @@ https://github.com/nlohmann/json
 用 C 从零创建一个简单的数据库
 https://github.com/cstack/db_tutorial
 
+'C++ Primer 中文版第5版学习笔记和课后练习答案'
+https://github.com/applenob/Cpp_Primer_Practice
+
 一个 C++ 头文件库，让你以简单的几行代码就可以实现高效的并发
 https://github.com/cpp-taskflow/cpp-taskflow
 
@@ -26,4 +29,7 @@ UNIX环境高级编程
 https://github.com/huaxz1986/APUE_notes
 
 分享 GitHub 上几个适合开发者跟进学习的课程资源
-https://mp.weixin.qq.com/s/D9OxgkXpEyvzg4wqMeYoPA
+https://mp.weixin.qq.com/s/D9OxgkXpEyvzg4wqMeYoPA
+
+【为你的开源代码选择合适的开源许可证】
+https://github.com/github/choosealicense.com
diff --git a/Collection.md b/Collection.md
@@ -26,4 +26,7 @@ https://weibo.com/tv/v/HBED7uD44?fid=1034:4389743065034223
 https://github.com/iptv-org/iptv
 
 '笔记/搜集/摘录/实践'
-https://github.com/jaywcjlove/handbook
+https://github.com/jaywcjlove/handbook
+
+'404 Not Found的知识库：计算机理论基础、计算机技术基础、底层研究、安全技术、安全研究、人工智能、企业安全建设、安全发展、职业规划、综合素质、国内外优秀技术人' 
+https://github.com/404notf0und/Always-Learning#%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD
diff --git a/Competition.md b/Competition.md
@@ -44,6 +44,12 @@ https://towardsdatascience.com/how-a-team-of-deep-learning-newbies-came-3rd-plac
 '阿水的开源比赛方案集锦’
 https://github.com/finlay-liu/kaggle_public
 
+“Kaggle开放日：Kaggle比赛教程”
+https://www.bilibili.com/video/av82395461/
+
+【Kaggle优胜方案汇总】“KaggleDB“
+https://kaggledb.com/solutions
+
 【开放 机器学习/数据科学/AI 竞赛大列表】
 https://github.com/mlcontests/mlcontests.github.io
 

diff --git a/ContinualLearning.md b/ContinualLearning.md
@@ -4,6 +4,9 @@ https://github.com/GMvandeVen/continual-learning
 A platform for online learning that curtails data latency and saves you cost.
 https://github.com/All-less/continuum
 
+Official code for ICLR 2020 paper "A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning."
+https://github.com/soochan-lee/CN-DPM
+
 Continual-Learning-Benchmark
 https://github.com/GT-RIPL/Continual-Learning-Benchmark
 

diff --git a/DLArch.md b/DLArch.md
@@ -100,9 +100,100 @@ https://github.com/alexandonian/pretorched-x
 NNI：微软发布的开源神经架构搜索/超参调优自动机器学习(AutoML)工具包，通过多种调优算法搜索最佳神经网络结构和(或)超参，支持单机、本地多机、云等不同的运行环境】’NNI (Neural Network Intelligence) - An open source AutoML toolkit for neural architecture search and hyper-parameter tuning' by Microsoft 
 https://github.com/Microsoft/nni/releases
 
+Implementation of the paper: "Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition" (ICCV 2019)
+https://github.com/HCPLab-SYSU/SSGRL
+
+Efficient Transformers for research, PyTorch and Tensorflow using Locality Sensitive Hashing
+https://github.com/cerebroai/reformers
+
+Code release for "Adversarial Robustness vs Model Compression, or Both?"
+https://github.com/yeshaokai/Robustness-Aware-Pruning-ADMM
+
+Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification
+https://github.com/hguosc/visual_attention_consistency
+
+Geom-GCN: Geometric Graph Convolutional Networks
+https://github.com/graphdml-uiuc-jlu/geom-gcn
+
+Code for ICLR 2020 paper 'AtomNAS: Fine-Grained End-to-End Neural Architecture Search'
+https://github.com/meijieru/AtomNAS
+
+Source code accompanying our CVPR 2019 paper: "NetTailor: Tuning the architecture, not just the weights." 
+https://github.com/pedro-morgado/nettailor
+
+Official pyTorch implementation of "Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks" experiments
+https://github.com/AlonShoshan10/dynamic_net
+
+R2D2: Reliable and Repeatable Detector and Descriptor
+https://github.com/naver/r2d2
+
+Multi-level Wavelet-CNN for Image Restoration
+https://github.com/lpj0/MWCNN
+
+Improved Wave-U-Net implemented in Pytorch
+https://github.com/f90/Wave-U-Net-Pytorch
+
+This is a tensorflow implementation of high-resolution representations for ImageNet classification.
+https://github.com/yuanyuanli85/tf-hrnet
+
+Codebase for Image Classification Research, written in PyTorch.
+https://github.com/facebookresearch/pycls
+
+Official code for using / reproducing CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge". 
+https://github.com/laura-rieger/deep-explanation-penalization
+
+partial residual networks
+https://github.com/WongKinYiu/PartialResidualNetworks
+
+SPOS(Single Path One-Shot Neural Architecture Search with Uniform Sampling) rebuilt in Pytorch with single GPU.
+https://github.com/ShunLu91/Single-Path-One-Shot-NAS
+
+This repository contains FCOS(ICCV'19) with VoVNet (CVPRW'19) efficient backbone networks. This code based on pytorch imeplementation of FCOS
+https://github.com/vov-net/VoVNet-FCOS
+
+NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning
+https://github.com/alexanderrichard/NeuralNetwork-Viterbi
+
+Graph Neural Networks for Multi-Label Classification
+https://github.com/QData/LaMP
+
+CORnet: Modeling the Neural Mechanisms of Core Object Recognition
+https://github.com/dicarlolab/CORnet
+
+The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
+https://github.com/Res2Net/Res2Net-PretrainedModels
+
+PyTorch code for our BMVC 2019 paper "Image Classification with Hierarchical Multigraph Networks" 
+https://github.com/bknyaz/bmvc_2019
+
+Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
+https://github.com/lrjconan/GRAN
+
+Bridging the gap Between Stability and Scalability in Neural Architecture Search
+https://github.com/xiaomi-automl/SCARLET-NAS
+
+A Pytorch implementation for the paper Local Relational Networks for Image Recognition 
+https://github.com/gan3sh500/local-relational-nets
+
+Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP-IJCNLP 19)
+https://github.com/INK-USC/KagNet
+
 PyTorch implementation of "Searching for A Robust Neural Architecture in Four GPU Hours", CVPR 2019
 https://github.com/D-X-Y/GDAS
 
+'《深入浅出图神经网络：GNN原理解析》配套代码' 
+https://github.com/FighterLYL/GraphNeuralNetwork
+
+'用Keras实现的图卷积神经网络  Semi-supervised classification by Graph Convolutional Network with Keras' 
+https://github.com/zhouchunpong/GCN_Keras
+
+【超参自动优化框架Optuna发布v1.0版】“Optuna: An open source hyperparameter optimization framework to automate hyperparameter search”
+https://github.com/optuna/optuna
+新特性：用最先进优化算法实现高效超参优化；支持各种机器学习库，包括PyTorch、TensorFlow、Keras、FastAI、Scikit-Learn、LightGBM和XGBoost；支持跨多台计算机并行执行，减少优化时间；搜索空间可用 Python 控制语句描述；集成多种可视化，方便对优化结果进行各种分析
+
+《FrequentNet : A New Deep Learning Baseline for Image Classification》
+https://arxiv.org/abs/2001.01034
+
 【女孩图片多标签分类】
 https://github.com/KichangKim/DeepDanbooru