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添加13,677字节、 2020年10月16日 (五) 01:12

创建页面，内容为“==前言== 过去一年，机器学习领域涌现出多篇重量级论文，其中一些技术已经有了表现上佳的项目实践。这里整理了50个年度…”

==前言==
过去一年，机器学习领域涌现出多篇重量级论文，其中一些技术已经有了表现上佳的项目实践。这里整理了50个年度最佳项目，涵盖图像处理、风格转换、图像分类、面部识别、视频防抖、目标检测、自动驾驶、智能推荐、游戏、下棋、医疗、语音生成、音乐、自然语言处理、预测等15个应用领域。AI 技术进展，一文尽览。
 
[[File:一m.png]]

==1.Image Manipulation 图像处理==

===No.1===
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
 
利用条件 GANs 的高分辨率图像合成及语义操作
 
项目地址：https://github.com/facebookresearch/fastText
 
[[File:1j.png]]

===No.2===
Using Deep Learning to Create Professional-Level Photographs
 
利用深度学习创作专业级照片
 
项目地址：https://research.googleblog.com/2017/07/using-deep-learning-to-create.html
 
[[File:2j.png]]

===No.3===
High Dynamic Range (HDR) Imaging using OpenCV (Python)
 
利用 OpenCV (Python)高动态范围成像(HDR)
 
项目地址：https://www.learnopencv.com/high-dynamic-range-hdr-imaging-using-opencv-cpp-python
 
[[File:3j.png]]

==2.Style Transfer 风格转换==

===No.4===
Visual Attribute Transfer through Deep Image Analogy
 
通过深度图像类比进行视觉属性转换（像素级别）
 
论文地址：https://arxiv.org/abs/1705.01088
 
[[File:4j.png]]

===No.5===
Deep Photo Style Transfer: A deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style
 
深度摄影风格转换: 一种处理大量图像内容，同时忠实传递参考风格的深度学习方法
 
论文地址：https://arxiv.org/abs/1703.07511
 
[[File:5j.png]]

===No.6===
Deep Image Prior:Super-resolution, inpainting, denoising without learning on a dataset and pretrained networks
 
Deep Image Prior：使用随机初始化神经网络实现图片去噪、超分辨率和修补
 
项目地址：https://dmitryulyanov.github.io/deep_image_prior
 
[[File:6j.png]]

==3.Image Classification 图像分类==

===No.7===
Feature Visualization: How neural networks build up their understanding of images
 
特征可视化: 神经网络如何建立对图像的理解
 
论文地址：https://distill.pub/2017/feature-visualization
 
[[File:7j.png]]

===No.8===
An absolute beginner's guide to Image Classification with Neural Networks
 
初学者指南：神经网络图像分类
 
项目地址：https://github.com/humphd/have-fun-with-machine-learning
 
[[File:8j.png]]

===No.9===
Background removal with deep learning
 
用深度学习清除图像背景
 
项目地址：https://medium.com/towards-data-science/background-removal-with-deep-learning-c4f2104b3157
 
[[File:9j.png]]

==4.Face Recgnition 面部识别==

===No.10===
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
 
基于直接体积回归的单幅图像大姿态三维人脸重建
 
项目地址：http://aaronsplace.co.uk/papers/jackson2017recon
 
[[File:10j.png]]

===No.11===
Eye blink detection with OpenCV, Python, and dlib
 
用 OpenCV，Python 和 dlib 捕捉眨眼动作
 
项目地址：http://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib
 
[[File:11j.png]]

===No.12===
DEAL WITH IT in Python with Face Detection
 
DEAL WITH IT：基于 Python 的人脸识别，实现自动戴墨镜效果
 
项目地址：https://www.makeartwithpython.com/blog/deal-with-it-generator-face-recognition
 
[[File:12个.gif]]

5.Video Stabilization 视频稳定

===No.13===
Fused Video Stabilization on the Pixel 2 and Pixel 2 XL
 
Google 解读 Pixel 2 和 Pixel 2 XL 两款手机的视频拍摄防抖技术
 
文章地址：https://research.googleblog.com/2017/11/fused-video-stabilization-on-pixel-2.html
 
[[File:13j.png]]

==6.Object Detection 目标检测==

===No.14===
How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras &amp
 
Not Hotdog：热狗识别器
 
项目地址：https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3
 
[[File:14j.png]]

===No.15===
Object detection: an overview in the age of Deep Learning
 
综述：深度学习时代的目标检测技术
 
文章地址：https://tryolabs.com/blog/2017/08/30/object-detection-an-overview-in-the-age-of-deep-learning
 
[[File:15j.png]]

===No.16===
How to train your own Object Detector with TensorFlow’s Object Detector API
 
如何使用 TensorFlow 的对象检测器 API 来训练你的对象检测器
 
文章地址：https://medium.com/towards-data-science/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9
 
[[File:16个.gif]]

===No.17===
Real-time object detection with deep learning and OpenCV
 
利用深度学习和 OpenCV 实时监测目标
 
项目地址：http://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv
 
[[File:17j.png]]

==7.Self Driving Car 自动驾驶==

===No.18===
Self-driving Grand Theft Auto V with Python : Intro [Part I] - Sentdex
 
用 Python 实现“侠盗飞车5”自动驾驶
 
视频地址：https://www.youtube.com/watch?v=ks4MPfMq8aQ
 
[[File:18j.png]]

===No.19===
Recognizing Traffic Lights With Deep Learning: How I learned deep learning in 10 weeks and won $5,000
 
用深度学习识别交通信号灯
 
文章地址：https://medium.freecodecamp.com/recognizing-traffic-lights-with-deep-learning-23dae23287cc
 
[[File:19j.png]]

==8.Recommendation AI 推荐系统==

===No.20===
Spotify’s Discover Weekly: How machine learning finds your new music
 
机器学习如何找到你喜欢的新歌
 
项目地址：https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe
 
[[File:20j.png]]

===No.21===
Artwork Personalization at Netflix
 
Netflix 上影视作品的个性化推荐
 
文章地址：https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76
 
[[File:21j.png]]

==9.Gaming AI 游戏 AI==

===No.22===
MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
 
MariFlow：在游戏中利用 RNN 自动驾驶马里奥卡丁车
 
视频地址：https://www.youtube.com/watch?v=Ipi40cb_RsI
 
[[File:22j.png]]

===No.23===
OpenAI Baselines: DQN. Reproduce reinforcement learning algorithms with performance on par with published results
 
OpenAI Baselines: DQN：强化学习算法的高质量复现
 
项目地址：https://blog.openai.com/openai-baselines-dqnv
 
[[File:23j.png]]

===No.24===
Reinforcement Learning on Dota 2 [Part II]
 
在 Dota 2 游戏中，强化学习算法如何击败人类玩家
 
项目地址：https://blog.openai.com/more-on-dota-2
 
[[File:24j.png]]

===No.25===
Creating an AI DOOM bot
 
在毁灭战士（DOOM）游戏中创建一个 AI 机器人
 
项目地址：https://www.codelitt.com/blog/doom-ai
 
[[File:25j.png]]

===No.26===
Phase-Functioned Neural Networks for Character Control
 
用于角色控制的相位函数神经网络
 
项目地址：http://theorangeduck.com/page/phase-functioned-neural-networks-character-control
 
[[File:26j.png]]

===No.27===
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI
 
用有监督的深度卷积神经网络实现游戏仿真
 
论文地址：https://arxiv.org/abs/1702.05663
 
[[File:27j.png]]

===No.28===
Introducing: Unity Machine Learning Agents
 
Unity 3D 游戏中的机器学习智能体
 
文章地址：https://blogs.unity3d.com/cn/2017/09/19/introducing-unity-machine-learning-agents
 
[[File:28j.png]]

==10.Chess AI 棋类 AI==

===No.29===
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
 
利用强化学习算法自我博弈，掌握国际象棋和日本将棋
 
论文地址：https://arxiv.org/abs/1712.01815
 
[[File:29j.png]]

===No.30===
AlphaGo Zero: Learning from scratch | DeepMind
 
AlphaGo Zero：从零开始学习，掌握围棋
 
论文地址：https://deepmind.com/blog/alphago-zero-learning-scratch
 
[[File:30j.png]]

===No.31===
How Does DeepMind' AlphaGo Zero Work?
 
讲解：DeepMind 的 AlphaGo Zero 是如何工作的？
 
视频地址：https://www.youtube.com/watch?v=vC66XFoN4DE
 
[[File:31j.png]]

===No.32===
A step-by-step guide to building a simple chess AI
 
实操指南：如何建立一个简单的国际象棋 AI
 
文章地址：https://medium.freecodecamp.com/simple-chess-ai-step-by-step-1d55a9266977
 
[[File:32j.png]]

==11.Medical AI 智能医疗==

===No.33===
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
 
AI 放射科医生：用深度学习技术分析胸透 x 光，检测是否患肺炎
 
项目地址：https://stanfordmlgroup.github.io/projects/chexnet
 
[[File:33j.png]]

===No.34===
Can you improve lung cancer detection? 2nd place solution for the Data Science Bowl 2017
 
如何改良对肺癌的检测？看2017年 kaggle 大赛第二名的解决方案
 
项目地址：http://juliandewit.github.io/kaggle-ndsb2017
 
[[File:34j.png]]

===No.35===
Improving Palliative Care with Deep Learning - Andrew Ng
 
用深度学习
 
项目地址：https://stanfordmlgroup.github.io/projects/improving-palliative-care
 
[[File:35j.png]]

===No.36===
Heart Disease Diagnosis with Deep Learning
 
用深度学习技术诊断心脏病
 
地址：https://blog.insightdatascience.com/heart-disease-diagnosis-with-deep-learning-c2d92c27e730
 
[[File:36j.png]]

==12.Speech AI 语音AI==

===No.37===
Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model - Data Scientists at Google
 
Tacotron：谷歌的全端到端语音合成技术
 
论文地址：https://arxiv.org/abs/1703.10135
 
[[File:37j.png]]

===No.38===
Sequence Modeling with CTC
 
用 CTC 算法进行序列建模
 
论文地址：https://distill.pub/2017/ctc
 
[[File:38j.png]]

===No.39===
Deep Voice: Real-time Neural Text-to-Speech - Baidu Silicon Valley AI Lab
 
Deep Voice：百度的实时语音生成技术
 
论文地址：https://arxiv.org/abs/1702.07825
 
[[File:39j.png]]

===No.40===
Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis - Apple
 
Deep Learning for Siri’s Voic：Siri 新声音背后的深度学习技术
 
文章地址：https://machinelearning.apple.com/2017/08/06/siri-voices.html
 
[[File:40j.png]]

==13.Music AI 音乐 AI==

===No.41===
Computer evolves to generate baroque music!
 
计算机自动生成巴洛克音乐
 
视频地址：https://www.youtube.com/watch?v=SacogDL_4JU
 
[[File:41j.png]]

===No.42===
Make your own music with WaveNets: Making a Neural Synthesizer Instrument
 
用 WaveNets 制作个性化音乐
 
地址：https://magenta.tensorflow.org/nsynth-instrument
 
[[File:42j.png]]

==14.NLP 自然语言处理==

===No.43===
Learning to communicate: Agents developing their own language - OpenAI Research
 
学会沟通：让 Agents 发明自己的语言
 
文章地址：https://openai.com/blog/learning-to-communicate
 
[[File:43j.png]]

===No.44===
Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow
 
图解机器学习：神经网络和 TensorFlow 文本分类
 
文章地址：https://medium.freecodecamp.org/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274
 
[[File:44j.png]]

===No.45===
A novel approach to neural machine translation - Facebook AI Research
 
一种神经机器翻译新方法
 
项目地址：https://code.facebook.com/posts/1978007565818999/a-novel-approach-to-neural-machine-translation
 
[[File:45j.png]]

===No.46===
How to make a racist AI without really trying
 
如何让 AI 辨识种族歧视言论
 
文章地址：https://blog.conceptnet.io/2017/07/13/how-to-make-a-racist-ai-without-really-trying
 
[[File:46j.png]]

==15.Prediction 预测==

===No.47===
Using Machine Learning to Predict Value of Homes On Airbnb
 
利用机器学习预测 Airbnb 住宅的价值
 
文章地址：https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d
 
[[File:47j.png]]

===No.48===
Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber
 
Uber神经网络时序预测不确定性估计
 
项目地址：https://eng.uber.com/neural-networks-uncertainty-estimation
 
[[File:48j.png]]

===No.49===
Using Machine Learning to make parking easier
 
使用机器学习让停车更容易
 
项目地址：https://research.googleblog.com/2017/02/using-machine-learning-to-predict.html
 
[[File:49j.png]]

===No.50===
How to Predict Stock Prices Easily - Intro to Deep Learning #7
 
深度学习入门教程：如何轻松预测股票价格
 
视频地址：https://www.youtube.com/watch?v=ftMq5ps503w
 
[[File:50j.png]]

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