[置顶] 机器学习、深度学习、计算机视觉、自然语言处理及应用案例——干货分享（持续更新......）

机器学习、深度学习、计算机视觉、自然语言处理及应用案例——干货分享（持续更新……）

author@jason_ql

http://blog.csdn.net/lql0716

1、机器学习/深度学习

1.1 对抗生成网络GAN

【2017.04.21】

对抗生成网络GAN变种大集合

【链接】

资源 | 生成对抗网络及其变体的论文汇总

【链接】

生成对抗网络(GAN)图片编辑

【链接】

CycleGAN失败案例

【链接】

【2017.04.22】

用条件生成对抗网络玩转中文书法

【链接】

《Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking》F Juefei-Xu, V N Boddeti, M Savvides [CMU & Michigan State University] (2017)

【链接】

【2017.04.23】

TP-GAN 让图像生成再获突破，根据单一侧脸生成正面逼真人脸

【链接】【GitHub】

【2017.04.26】

【对抗生成网络GAN教程】

《Tutorial on GANs》by Adit Deshpande

【链接】【GitHub】

【2017.05.07】

【GAN相关资源与实现】’Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN’ by YadiraF

【链接】【GitHub】

【PyTorch实现的CoGAN】《Coupled Generative Adversarial Networks》M Liu, O Tuzel [Mitsubishi Electric Research Labs (MERL)] (2016)

【链接】【GitHub】

【利用CGAN生成Sketch漫画】《Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks》Y Liu, Z Qin, Z Luo, H Wang [Beihang University & Samsung Telecommunication Research Institute] (2017)

【链接】【GitHub】

《Adversarial Feature Learning》J Donahue, P Krähenbühl, T Darrell [UC Berkeley]

【链接】【GitHub】

【PyTorch实现的DCGAN、pix2pix、DiscoGAN、CycleGAN、BEGAN VAE、Neural Style Transfer、Char RNN等】’Paper Implementations - Use PyTorch to implement some classic frameworks’ by SunshineAtNoon

【链接】【GitHub】

【GAN画风迁移】《Generative Adversarial Networks for Style Transfer (LIVE) - YouTube》by Siraj Raval

【链接】【GitHub】【video】

【2017.05.08】

生成对抗网络（GAN）研究年度进展评述

【链接】【GitHub】

【对抗生成网络(Gan)深入研究(文献/教程/模型/框架/库等)】《Delving deep into GANs》by Grigorios Kalliatakis

【链接】【GitHub】

【对抗式机器翻译】《Adversarial Neural Machine Translation》L Wu, Y Xia, L Zhao, F Tian, T Qin, J Lai, T Liu [Sun Yat-sen University & University of Science and Technology of China & Microsoft Research Asia] (2017)

【链接】【GitHub】

【CycleGAN生成模型：熊变熊猫】’Models generated by CycleGAN’ by Tatsuya

【链接】【GitHub】

【对抗生成网络(GAN)】《Generative Adversarial Networks (LIVE) - YouTube》by Siraj Raval

【链接】【GitHub】【video】

【Keras实现的ACGAN/DCGAN】’Implementation of some basic GAN architectures in Keras’ by Batchu Venkat Vishal

【链接】【GitHub】

【2017.05.09】

【策略梯度SeqGAN】《SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient》L Yu, W Zhang, J Wang, Y Yu [Shanghai Jiao Tong University & University College London] (2016)

【链接】【GitHub】

【2017.05.10】

《Improved Training of Wasserstein GANs》I Gulrajani, F Ahmed, M Arjovsky, V Dumoulin, A Courville [Montreal Institute for Learning Algorithms & Courant Institute of Mathematical Sciences] (2017)

【链接】【GitHub】【GitHub2】

《Geometric GAN》J H Lim, J C Ye [ETRI & KAIST] (2017)

【链接】【GitHub】

【PyTorch实现的CycleGAN/SGAN跨域迁移(MNIST-to-SVHN & SVHN-to-MNIST)】’PyTorch Implementation of CycleGAN and SGAN for Domain Transfer (Minimal)’ by yunjey GitHub:

【链接】【GitHub】

1.2 神经网络

【2017.04.24】

如何用PyTorch实现递归神经网络？

【链接】【GitHub】

【2017.04.25】

一个基于TensorFlow的简单故事生成案例：带你了解LSTM

【链接】【GitHub】

【2017.05.07】

深度学习10大框架对比分析

【链接】【GitHub】

深度学习之CNN卷积神经网络

【链接】【GitHub】

【Keras教程：Python深度学习】《Keras Tutorial: Deep Learning in Python》by Karlijn Willems

【链接】【GitHub】

TensorFlow 官方解读：如何在多系统和网络拓扑中构建高性能模型

【链接】【GitHub】

从自编码器到生成对抗网络：一文纵览无监督学习研究现状

【链接】【GitHub】

《Residual Attention Network for Image Classification》F Wang, M Jiang, C Qian, S Yang, C Li, H Zhang, X Wang, X Tang [SenseTime Group Limited & Tsinghua University & The Chinese University of Hong Kong] (2017)

【链接】【GitHub】

-【基于OpenAI Gym/Tensorflow/Keras的增强学习实验平台】’OpenAI Lab - An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.’ by Wah Loon Keng

【链接】【GitHub】

【基于生成卷积网络的潜在指纹重建】《Generative Convolutional Networks for Latent Fingerprint Reconstruction》J Svoboda, F Monti, M M. Bronstein [USI Lugano] (2017)

【链接】【GitHub】

【TensorFlow入门代码集锦】’tensorflow-resources - Curated Tensorflow code resources to help you get started’ by Skcript

【链接】【GitHub】

入门级攻略：机器学习 VS. 深度学习

【链接】【GitHub】

《Gabor Convolutional Networks》S Luan, B Zhang, C Chen, X Cao, J Han, J Liu [Beihang University & University of Central Florida Orlando & Northumbria University & Huawei Company] (2017)

【链接】【GitHub】

TensorFlow基准：图像分类模型在各大平台的测试研究

【链接】【GitHub】

谷歌开源深度学习街景文字识别模型：让地图随世界实时更新

【链接】【GitHub】

《Geometric deep learning: going beyond Euclidean data》M M. Bronstein, J Bruna, Y LeCun, A Szlam, P Vandergheynst [USI Lugano & NYU & Facebook AI Research] (2016)

【链接】【GitHub】

【利用强化学习设计神经网络架构】《Designing Neural Network Architectures using Reinforcement Learning》B Baker, O Gupta, N Naik, R Raskar [MIT] (2016)

【链接】【GitHub】

【神经网络：三万英尺高空纵览入门】《Neural Networks : A 30,000 Feet View for Beginners | Learn OpenCV》by Satya Mallick

【链接】【GitHub】

Top100论文导读：深入理解卷积神经网络CNN（Part Ⅰ）

【链接】【GitHub】

Top100论文导读：深入理解卷积神经网络CNN（Part Ⅱ）

【链接】【GitHub】

-【深度神经网络权值初始化的研究】《On weight initialization in deep neural networks》S K Kumar (2017)

【链接】【GitHub】

【2017.05.08】

【提升结构化特征嵌入深度度量学习】《Deep Metric Learning via Lifted Structured Feature Embedding》H Oh Song, Y Xiang, S Jegelka, S Savarese (2016)

【链接】【GitHub】

【图的深度特征学习】《Deep Feature Learning for Graphs》R A. Rossi, R Zhou, N K. Ahmed [Palo Alto Research Center (Xerox

PARC) & Intel Labs] (2017)

【链接】【GitHub】

【用于性能分析、模型优化的神经网络生成器】’Perceptron - A flexible artificial neural network builder to analysis performance, and optimise the best model.’ by Caspar Wylie

【链接】【GitHub】

【TensorFlow最佳实践之文件、文件夹与模型架构实用建议】《TensorFlow: A proposal of good practices for files, folders and models architecture》by Morgan

【链接】【GitHub】

【带有快速局部滤波的图CNN】《Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering》M Defferrard, X Bresson, P Vandergheynst [EPFL] (2016)

【链接】【GitHub】

【(Tensorflow/TFLearn)RNN命名实体识别】“Named Entity Recognition using Recurrent Neural Networks in Tensorflow and TFLearn” by Dhwaj Raj

【链接】【GitHub】

【深度学习的局限性】《Failures of Deep Learning》S Shalev-Shwartz, O Shamir, S Shammah [The Hebrew University & Weizmann Institute] (2017)

【链接】【GitHub】【video】

【基于矩阵乘法的并行多通道卷积】《Parallel Multi Channel Convolution using General Matrix Multiplication》A Vasudevan, A Anderson, D Gregg [Trinity College Dublin] (2017)

【链接】【GitHub】

【在手机上进行深度学习训练】《Migrate Deep Learning Training onto Mobile Devices!》by Saman BigManborn

【链接】【GitHub】

【TensorFlow实现的RNN(LSTM)序列预测】’tensorflow-lstm-regression - Sequence prediction using recurrent neural networks(LSTM) with TensorFlow’ by mouradmourafiq

【链接】【GitHub】

【TensorFlow 1.1.0发布】”TensorFlow 1.1.0 Released”

【链接】【GitHub】

【CNN到图结构数据的推广】《A Generalization of Convolutional Neural Networks to Graph-Structured Data》Y Hechtlinger, P Chakravarti, J Qin [CMU] (2017)

【链接】【GitHub】

Momenta研发总监任少卿：From Faster R-CNN to Mask R-CNN

【链接】【GitHub】

《Deep Multitask Learning for Semantic Dependency Parsing》H Peng, S Thomson, N A. Smith [CMU] (2017)

【链接】【GitHub】

【利用整流单元稀疏性加快卷积神经网络】《Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units》S Shi, X Chu [Hong Kong Baptist University] (2017)

【链接】【GitHub】

【深度学习之CNN卷积神经网络】《Deep Learning #2: Convolutional Neural Networks》by Rutger Ruizendaal

【链接】【GitHub】

【PyTorch试炼场：提供各主流预训练模型】’pytorch-playground - Base pretrained model and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)’ by Aaron Chen

【链接】【GitHub】

从自编码器到生成对抗网络：一文纵览无监督学习研究现状

【链接】【GitHub】

【2017.05.09】

Learning Deep Learning with Keras

【链接】【GitHub】

【TensorFlow生成模型库】’A Library for Generative Models’

【链接】【GitHub】

【深度学习的过去、现在和未来】《Deep Learning – Past, Present, and Future》by Henry H. Eckerson

【链接】【GitHub】

正在涌现的新型神经网络模型：优于生成对抗网络

【链接】【GitHub】

【贝叶斯深度学习文献列表】’A curated list of resources dedicated to bayesian deep learning’ by Rabindra Nath Nandi

【链接】【GitHub】

【面向推荐系统的深度学习文献列表】’Deep-Learning-for-Recommendation-Systems - Deep Learning based articles , paper and repositories for Recommender Systems’ by Rabindra Nath Nandi

【链接】【GitHub】

【2017.05.10】

【深度学习职位面试经验分享】《My deep learning job interview experience sharing》by Justin Ho

【链接】【GitHub】

《Convolutional Sequence to Sequence Learning》J Gehring, M Auli, D Grangier, D Yarats, Y N. Dauphin [Facebook AI Research] (2017)

【链接】【GitHub】

【VGG19的TensorFlow实现/详解】’VGG19_with_tensorflow - An easy implement of VGG19 with tensorflow, which has a detailed explanation.’ by Jipeng Huang

【链接】【GitHub】

【Keras实现的深度聚类】“Keras implementation of Deep Clustering paper” by Eduardo Silva

【链接】【GitHub】

1.3 机器学习

【2017.05.07】

【无监督学习纵览】《Navigating the Unsupervised Learning Landscape》by Eugenio Culurciello

【链接】【GitHub】

【(Python)机器学习导论课程资料】’Materials for the “Introduction to Machine Learning” class’ by Andreas Mueller

【链接】【GitHub】

【Newton ADMM快速准平滑牛顿法】’A Newton ADMM based solver for Cone programming.’

【链接】【GitHub】

【超大规模机器学习工具集MaTEx】’Machine Learning Toolkit for Extreme Scale (MaTEx) - a collection of high performance parallel machine learning and data mining (MLDM) algorithms, targeted for desktops, supercomputers and cloud computing systems’

【链接】【GitHub】

关于迁移学习的一些资料

【链接】【GitHub】

《Clustering with Adaptive Structure Learning: A Kernel Approach》Z Kang, C Peng, Q Cheng [Southern Illinois University] (2017)

【链接】【GitHub】

【(R)稀疏贝叶斯网络学习】’sparsebn - Software for learning sparse Bayesian networks’ by Bryon Aragam

【链接】【GitHub】

【Node.js机器学习/自然语言处理/情感分析工具包】’salient - Machine Learning, Natural Language Processing and Sentiment Analysis Toolkit for Node.js’ by Thomas Holloway

【链接】【GitHub】

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

【链接】【GitHub】

机器学习中容易犯下的错

【链接】【GitHub】

【2017.05.08】

【(C/C++ and MATLAB/Octave)互信息函数工具箱】’MIToolbox - Mutual Information functions for C and MATLAB’ by Adam Pocock

【链接】【GitHub】

【Criteo 1TB数据集上多机器学习算法Benchmark】’Benchmark of different ML algorithms on Criteo 1TB dataset’ by Rambler Digital Solutions

【链接】【GitHub】

机器学习十大常用算法

【链接】【GitHub】

【加速随机梯度下降】《Accelerating Stochastic Gradient Descent》P Jain, S M. Kakade, R Kidambi, P Netrapalli, A Sidford [Microsoft Research & University of Washington & Stanford University] (2017)

【链接】【GitHub】

【(C++)大规模稀疏矩阵分解包】“LIBMF - library for large-scale sparse matrix factorization” by cjlin1

【链接】【GitHub】

【(C/Python/Matlab)求解大规模正则线性分类与回归的简单包】“LIBLINEAR - simple package for solving large-scale regularized linear classification and regression” by cjlin1

【链接】【GitHub】

【批量归一化(Batch Norm)概述】《Appendix: A Batch Norm Overview》by alexirpan

【链接】【GitHub】

【2017.05.09】

谱聚类

【链接】【GitHub】

【2017.05.10】

【学习非极大值抑制】《Learning non-maximum suppression》J Hosang, R Benenson, B Schiele [Max Planck Institut für Informatik] (2017)

【链接】【GitHub】

【(Python)机器学习工作流框架】’AlphaPy - Machine Learning Pipeline for Python’ by ScottFree Analytics

【链接】【GitHub】

【如何解释机器学习模型和结果】《Ideas on interpreting machine learning | O’Reilly Media》by Patrick HallWen Phan, SriSatish Ambati

【链接】【GitHub】

2、计算机视觉

【2017.04.21】

OpenCV/Python/dlib人脸关键点实时标定

【paper】【GitHub】

【2017.04.22】

【高效的卷积神经网络在手机中的应用】MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

【paper】【GitHub】

【生成式人脸补全】《Generative Face Completion》Y Li, S Liu, J Yang, M-H Yang [Univerisity of California, Merced & Adobe Research] (2017)

【paper】【GitHub】

《Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art》J Janai, F Güney, A Behl, A Geiger [Max Planck Institute for Intelligent Systems & ETH Zurich] (2017)

【paper】【GitHub】

《Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking》L Leal-Taixé, A Milan, K Schindler, D Cremers, I Reid, S Roth [Technical University Munich & University of Adelaide & ETH Zurich & TU Darmstadt] (2017)《译：多目标追踪的现状分析》

【paper】【GitHub】

《CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction》K Tateno, F Tombari, I Laina, N Navab [CAMP - TU Munich] (2017)

【paper】【GitHub】

《Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields》Z Cao, T Simon, S Wei, Y Sheikh [CMU] (2016)《译：基于PAF的实时二维姿态估计》

【paper】【GitHub】

《Virtual to Real Reinforcement Learning for Autonomous Driving》Y You, X Pan, Z Wang, C Lu [Shanghai Jiao Tong University & UC Berkeley & Tsinghua University] (2017)

【paper】【GitHub】

《Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark》T Hackel, N Savinov, L Ladicky, J D. Wegner, K Schindler, M Pollefeys [ETH Zurich] (2017)

【paper】【GitHub】

《Learning Video Object Segmentation with Visual Memory》P Tokmakov, K Alahari, C Schmid [Inria] (2017)

【paper】【GitHub】

【2017.04.23】

《A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN》by Dhruv Parthasarathy

【paper】【GitHub】

《Stacked Hourglass Networks for Human Pose Estimation》A Newell, K Yang, J Deng [University of Michigan] (2016)

【paper】【GitHub】

自动驾驶计算机视觉研究综述：难题、数据集与前沿成果（附67页论文下载）

【paper】【GitHub】

谷歌推出最新“手机版”视觉应用的卷积神经网络—MobileNets

【paper】【GitHub】

《Deep Learning for Photo Editing》by Malte Baumann

【paper】【GitHub】

【2017.04.24】

TensorFlow Implementation of conditional variational auto-encoder (CVAE) for MNIST by hwalsuklee

【paper】【GitHub】

【2017.04.26】

【单目视频深度帧间运动估计无监督学习框架】’SfMLearner - An unsupervised learning framework for depth and ego-motion estimation from monocular videos’ by T Zhou

【paper】【GitHub】

“U-Nets(Caffe)”

【paper】【GitHub】

《U-Net: Convolutional Networks for Biomedical Image Segmentation》(2015)

【paper】【GitHub】

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

【paper】【GitHub】

【2017.05.07】

【(C++/Matlab)视频/图片序列人脸标定】’Find Face Landmarks - C++ \ Matlab library for finding face landmarks and bounding boxes in video\image sequences.’ by Yuval Nirkin

【paper】【GitHub】

【(Keras)UNET图像分割】’ZF_UNET_224 Pretrained Model - Modification of convolutional neural net “UNET” for image segmentation in Keras framework’ by ZFTurbo

【paper】【GitHub】

【复杂条件下的深度人脸分割】”Deep face segmentation in extremely hard conditions” by Yuval Nirkin

【paper】【GitHub】

【基于单目RGB图像的实时3D人体姿态估计】《VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera》D Mehta, S Sridhar, O Sotnychenko… [Max Planck Institute for Informatics & Universidad Rey Juan Carlos] (2017)

【paper】【paper2】【GitHub】

【衣服检测与识别】《DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations》Z Liu, P Luo, S Qiu, X Wang, X Tang (CVPR 2016)

【paper】

【paper2】【GitHub】

SLAM 学习与开发经验分享

【paper】【GitHub】

【大规模街道级图片(分割)数据集】《Releasing the World’s Largest Street-level Imagery Dataset for Teaching Machines to See》by Peter Kontschieder

【paper】【GitHub】【dataset】

【基于深度增强学习的交叉路口车辆自动导航】《Navigating Intersections with Autonomous Vehicles using Deep Reinforcement Learning》D Isele, A Cosgun, K Subramanian, K Fujimura [University of Pennsylvania & Honda Research Institute & Georgia Institute of Technology] (2017)

【paper】【GitHub】

十分钟看懂图像语义分割技术

【paper】【GitHub】

【(C++)实时多人关键点检测】’OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library’

【paper】【GitHub】

计算机视觉、机器学习相关领域论文和源代码大集合

【paper】【GitHub】

【(Tensorflow)RPN+人体检测】’RPNplus - RPN+(Tensorflow) for people detection’ by Shiyu Huang

【paper】【GitHub】

【(C++/OpenCV3)实时可变人脸追踪】’Real time deformable face tracking in C++ with OpenCV 3.’ by Kyle McDonald

【paper】【GitHub】

【图片快速标记】《How to Label Images Quickly 》by Pete Warden

【paper】【paper2】【GitHub】

【基于深度图像类比的视觉要素迁移】《Visual Attribute Transfer through Deep Image Analogy》J Liao, Y Yao, L Yuan, G Hua, S B Kang [Microsoft Research & Shanghai Jiao Tong University] (2017)

【paper】【GitHub】

【基于深度学习的质谱成像中的肿瘤分类】《Deep Learning for Tumor Classification in Imaging Mass Spectrometry》J Behrmann, C Etmann, T Boskamp, R Casadonte, J Kriegsmann, P Maass [University of Bremen & Proteopath GmbH] (2017)

【paper】【link2】【GitHub】

【Andorid手机上基于TensorFlow的人体行为识别】《Deploying Tensorflow model on Andorid device for Human Activity Recognition》by Aaqib Saeed

【paper】【paper2】【GitHub】

【TensorFlow图像自动描述】《Caption this, with TensorFlow | O’Reilly Media》by Raul Puri, Daniel Ricciardelli

【paper】【paper2】【GitHub】

【基于CNN (InceptionV1) + STFT的Kaggle鲸鱼检测竞赛方案】’CNN (InceptionV1) + STFT based Whale Detection Algorithm - A whale detector design for the Kaggle whale-detector challenge!’ by Tarin Ziyaee

【paper】【GitHub】

【TensorFlow实现的摄像头pix2pix图图转换】’webcam-pix2pix-Tensorflow - Source code and pretrained model for webcam pix2pix’ by Memo Akten

【paper】【GitHub】

【图像分类的大规模进化】《Large-Scale Evolution of Image Classifiers》E Real, S Moore, A Selle, S Saxena, Y L Suematsu, Q Le, A Kurakin [Google Brain & Google Research] (2017)

【paper】【paper2】【GitHub】

【2017.05.08】

人脸检测与识别的趋势和分析

【paper】【GitHub】

【全局/局部一致图像补全】《Globally and Locally Consistent Image Completion》S Iizuka, E Simo-Serra, H Ishikawa (2017)

【paper】【GitHub】

【基于CNN的面部表情识别】《Convolutional Neural Networks for Facial Expression Recognition》S Alizadeh, A Fazel [Stanford University] (2017)

【paper】【GitHub】

计算机视觉识别简史：从 AlexNet、ResNet 到 Mask RCNN

【paper】【GitHub】

【脸部识别与聚类】《Face Identification and Clustering》A Dhingra [The State University of New Jersey] (2017)

【paper】【GitHub】

【(TensorFlow)通用U-Net图像分割】’Tensorflow Unet - Generic U-Net Tensorflow implementation for image segmentation’ by Joel Akeret

【paper】【GitHub】

【深度学习介绍之文本图像生成】《How to Convert Text to Images - Intro to Deep Learning #16 - YouTube》by Siraj Raval

【paper】【GitHub】

【一个深度神经网络如何对自动驾驶做端到端的训练】《Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car》M Bojarski, P Yeres, A Choromanska, K Choromanski, B Firner, L Jackel, U Muller [NVIDIA Corporation & New York University & Google Research] (2017)

【paper】【GitHub】

【基于深度卷积网络的动态场景关节语义与运动分割】《Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks》N Haque, N D Reddy, K. M Krishna [International Institute of Information Technology & Max Planck Institute For Intelligent Systems] (2017)

【paper】【GitHub】

【高分辨率图像的实时语义分割】《ICNet for Real-Time Semantic Segmentation on High-Resolution Images》H Zhao, X Qi, X Shen, J Shi, J Jia [The Chinese University of Hong Kong & SenseTime Group Limited] (2017)

【paper】【GitHub】【GitHub2】【video】

【深度学习应用到语义分割的综述】《A Review on Deep Learning Techniques Applied to Semantic Segmentation》A Garcia-Garcia, S Orts-Escolano, S Oprea, V Villena-Martinez, J Garcia-Rodriguez [University of Alicante] (2017)

【paper】【GitHub】

【医学图像的深度迁移学习的原理】《Understanding the Mechanisms of Deep Transfer Learning for Medical Images》H Ravishankar, P Sudhakar, R Venkataramani, S Thiruvenkadam, P Annangi, N Babu, V Vaidya [GE Global Research] (2017)

【paper】【GitHub】

【(Torch)基于循环一致对抗网络的非配对图到图翻译】

【paper】【GitHub】

【深度网络光流估计的演化】《FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks》E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox [University of Freiburg] (2016)

【paper】【GitHub】【video】

【基于p-RNN的目标实例标注】《Annotating Object Instances with a Polygon-RNN》L Castrejon, K Kundu, R Urtasun, S Fidler [University of Toronto] (2017)

【paper】【GitHub】

《Dataset Augmentation for Pose and Lighting Invariant Face Recognition》D Crispell, O Biris, N Crosswhite, J Byrne, J L. Mundy [Vision Systems, Inc & Systems and Technology Research] (2017)

【paper】【GitHub】

【人脸的分割、交换与感知】《On Face Segmentation, Face Swapping, and Face Perception》Y Nirkin, I Masi, A T Tran, T Hassner, G Medioni [The Open University of Israel & USC] (2017)

【paper】【GitHub】

【面向视频运动估计的几何感知神经网络SfM-Net】《SfM-Net: Learning of Structure and Motion from Video》S Vijayanarasimhan, S Ricco, C Schmid, R Sukthankar, K Fragkiadaki [Google & Indri & CMU] (2017)

【paper】【GitHub】

【基于深度自学习的弱监督目标定位】《Deep Self-Taught Learning for Weakly Supervised Object Localization》Z Jie, Y Wei, X Jin, J Feng, W Liu [Tencent AI Lab & National University of Singapore] (2017)

【paper】【GitHub】

【单个图像的手部关键点检测】《Hand Keypoint Detection in Single Images using Multiview Bootstrapping》T Simon, H Joo, I Matthews, Y Sheikh [CMU] (2017)

【paper】【GitHub】

《Hierarchical 3D fully convolutional networks for multi-organ segmentation》H R. Roth, H Oda, Y Hayashi, M Oda, N Shimizu, M Fujiwara, K Misawa, K Mori [Nagoya University & Nagoya University Graduate School of Medicine & Aichi Cancer Center] (2017)

【paper】【GitHub】

《Towards Large-Pose Face Frontalization in the Wild》X Yin, X Yu, K Sohn, X Liu, M Chandraker [Michigan State University & NEC Laboratories America & University of California, San Diego] (2017)

【paper】【paper2】【GitHub】

【通过观察目标运动迁移学习特征】《Learning Features by Watching Objects Move》D Pathak, R Girshick, P Dollár, T Darrell, B Hariharan [Facebook AI Research & UC Berkeley] (2016)

【paper】【GitHub】

【面向深度学习训练的视频标记工具】’BeaverDam - Video annotation tool for deep learning training labels’ by Anting Shen

【paper】【GitHub】

【生成对抗网络(GAN)图片编辑】《Photo Editing with Generative Adversarial Networks | Parallel Forall》by Greg Heinrich

【paper】【paper2】【GitHub】

解读Keras在ImageNet中的应用：详解5种主要的图像识别模型

【paper】【GitHub】

《Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation》Y Chen, C Shen, X Wei, L Liu, J Yang [Nanjing University of Science and Technology & The University of Adelaide & Nanjing University] (2017)

【paper】【GitHub】

【结构感知卷积网络的人体姿态估计】《Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation》Y Chen, C Shen, X Wei, L Liu, J Yang [Nanjing University of Science and Technology & The University of Adelaide & Nanjing University] (2017)

【paper】【GitHub】

【基于神经网络的鲁棒多视角行人跟踪】《Robust Multi-view Pedestrian Tracking Using Neural Networks》M Z Alom, T M. Taha [University of Dayton] (2017)

【paper】【GitHub】

【视频密集事件描述】”Dense-Captioning Events in Videos”

【paper】【GitHub】【data】

【受Siraj Raval深度学习视频启发的每周深度学习实践挑战】’Deep-Learning Challenges - Codes for weekly challenges on Deep Learning by Siraj’ by Batchu Venkat Vishal

【paper】【GitHub】

《SLAM with Objects using a Nonparametric Pose Graph》B Mu, S Liu, L Paull, J Leonard, J How [MIT] (2017)

【paper】【GitHub】

【医学图像分割中迭代估计的归一化输入】《Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation》M Drozdzal, G Chartrand, E Vorontsov, L D Jorio, A Tang, A Romero, Y Bengio, C Pal, S Kadoury [Universite de Montreal & Imagia Inc] (2017)

【paper】【GitHub】

《An Analysis of Action Recognition Datasets for Language and Vision Tasks》S Gella, F Keller [University of Edinburgh] (2017)

【paper】【GitHub】

【2017.05.09】

Tensorflow实现卷积神经网络，用于人脸关键点识别

【paper】【GitHub】

【FRCN(faster-rcnn)文字检测】’Text-Detection-using-py-faster-rcnn-framework’ by jugg1024

【paper】【GitHub】

【手机单目视觉状态估计器】’VINS-Mobile - Monocular Visual-Inertial State Estimator on Mobile Phones’ by HKUST Aerial Robotics Group

【paper】【GitHub】

【R-FCN目标检测】R-FCN: Object Detection via Region-based Fully Convolutional Networks

【paper】【GitHub】

行人检测、跟踪与检索领域年度进展报告

【paper】【GitHub】

【(TensorFlow)点云(Point Cloud)分类、分割、场景语义理解统一框架PointNet】’PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation’

【paper】【paper2】【GitHub】【GitHub2】

【深度视频去模糊】《Deep Video Deblurring》by Shuochen Su(2016)

【paper】【paper2】【GitHub】【video】

【中国的Infervision及其肺癌诊断AI工具】《Chinese startup Infervision emerges from stealth with an AI tool for diagnosing lung cancer | TechCrunch》by Jonathan Shieber

【paper】【paper2】【GitHub】

【基于医院大量胸部x射线数据库的弱监督分类和常见胸部疾病定位的研究】《ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases》X Wang, Y Peng, L Lu, Z Lu… [National Institutes of Health] (2017)

【paper】【paper2】【GitHub】

目标跟踪方法的发展概述

【paper】【GitHub】

【(Caffe)实时交互式图片自动着色】《Real-Time User-Guided Image Colorization with Learned Deep Priors》[UC Berkeley] (2017)

【paper】【paper2】【GitHub】【video】

相术的新衣】《Physiognomy’s New Clothes》by Blaise Aguera y Arcas

【paper】【GitHub】

【2017.05.10】

快速生成人脸模型

【paper】【paper2】【GitHub（预计八月开源）】

VALSE2017系列之二: 边缘检测领域年度进展报告

【paper】【GitHub】

【(GTC2017)Stanford发布0.5PB大规模放射医疗图像ImageNet数据集】“Stanford gave the world ImageNet. Now it’s giving the world Medical ImageNet—a 0.5PB dataset for diagnostic radiology” via:James Wang

【paper】【GitHub】

【医疗图像深度学习】《Medical Image Analysis with Deep Learning》by Taposh Dutta-Roy

Part1

Part2

Part3

【激光雷达(LIDAR)：自驾车关键传感器】《An Introduction to LIDAR: The Key Self-Driving Car Sensor》by Oliver Cameron

【paper】【GitHub】

【根据目标脸生成带语音的视频】《You said that?》J S Chung, A Jamaludin, A Zisserman [University of Oxford] (2017)

【paper】【GitHub】

【用于图像生成和数据增强的生成协作网】《Generative Cooperative Net for Image Generation and Data Augmentation》Q Xu, Z Qin, T Wan [Beihang University & Alibaba Group] (2017)

【paper】【GitHub】

【COCO像素级标注数据集】’The official homepage of the COCO-Stuff dataset.’

【paper】【GitHub】

《COCO-Stuff: Thing and Stuff Classes in Context》 (2017) 【paper】【GitHub】

【LinkNet：基于编码器表示的高效语义分割】《(LinkNet)Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation》A Chaurasia, E Culurciello

【paper】【GitHub】【GitHub2】

3、自然语言处理

【2017.04.22】

《Semantic Instance Segmentation via Deep Metric Learning》A Fathi, Z Wojna, V Rathod, P Wang, H O Song, S Guadarrama, K P. Murphy [Google Inc & UCLA] (2017)

【paper】【GitHub】

【2017.04.26】

【对话语料集】’chat corpus collection from various open sources’ by Marsan-Ma

【paper】【GitHub】

【2017.05.07】

【从文本中提取特征的神经网络技术综述】《A Survey of Neural Network Techniques for Feature Extraction from Text》V John [University of Waterloo] (2017)

基於向量匹配的情境式聊天機器人’ by Justin Yang

【paper】【GitHub】

【PyTorch实践：序列到序列Attention法-英翻译】《Practical PyTorch: Translation with a Sequence to Sequence Network and Attention》by Sean Robertson

【paper】【GitHub】

【PyTorch实践：探索GloVe词向量】《Practical PyTorch: Exploring Word Vectors with GloVe》by Sean Robertson

【paper】【GitHub】

【自然语言生成(NLG)系统评价指标】《How to do an NLG Evaluation: Metrics》by Ehud Reiter

【paper】【paper2】【GitHub】

【看似靠谱的文本分类对抗样本】’textfool - Plausible looking adversarial examples for text classification’ by Bogdan Kulynych >【paper】【GitHub】

【基于bidirectional GRU-CRF的联合中文分词与词性标注】’A Joint Chinese segmentation and POS tagger based on bidirectional GRU-CRF’ by yanshao9798

【paper】【GitHub】

【自然语言处理(NLP)入门指南】《How to get started in NLP》by Melanie Tosik

【paper】【GitHub】

【2017.05.08】

【(TensorFlow)面向文本相似度检测的Deep LSTM siamese网络】’Deep LSTM siamese network for text similarity - Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character embeddings’ by Dhwaj Raj

【paper】【GitHub】

【Keras/TensorFlow语种检测】《Deep Learning: Language identification using Keras & TensorFlow》by Lucas KM

【paper】【GitHub】

-【(C++)神经网络语种检测工具】“Compact Language Detector v3 (CLD3) - neural network model for language identification” by Google

【paper】【GitHub】

【用于文本分类的端到端多视图网络】《End-to-End Multi-View Networks for Text Classification》H Guo, C Cherry, J Su [National Research Council Canada] (2017)

【paper】【GitHub】

【理解非结构化文本数据】《Making Sense of Unstructured Text Data》L Li, W M. Campbell, C Dagli, J P. Campbell [MIT Lincoln Laboratory] (2017)

【paper】【GitHub】

【非本族语者英语写作风格检测】《Detecting English Writing Styles For Non Native Speakers》Y Chen, R Al-Rfou’, Y Choi [Stony Brook University] (2017)

【paper】【GitHub】

【2017.05.10】

Facebook提出全新CNN机器翻译：准确度超越谷歌而且还快九倍（已开源）

【paper1】【paper2】【GitHub】

4、应用案例

【2017.04.21】

深度学习入门实战（一）-像Prisma一样算法生成梵高风格画像

【paper】【GitHub】

【2017.04.22】

我们教电脑识别视频字幕

【paper】【GitHub】

【2017.04.24】

《Data Sciencing Motorcycles: Lean Assist》by Josh Peng

【paper】【GitHub】

【2017.04.26】

【PhotoScan新增的去除翻拍反光功能】《PhotoScan: Taking Glare-Free Pictures of Pictures | Google Research Blog》by Ce Liu, Michael Rubinstein, Mike Krainin, Bill Freeman

【paper】【GitHub】

【2017.05.08】

【假新闻的实时检测】《How to Detect Fake News in Real-Time 》by Krishna Bharat

【paper】【GitHub】

5、综合

5.1 教程

【2017.04.21】

30 Free Courses: Neural Networks, Machine Learning, Algorithms, AI

【paper】【GitHub】

【2017.04.22】

【Deep Learning】

英文原文：【link】

中文译文：【link】

中文译文说明：【link】

【2017.04.23】

机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 1)

【paper】【GitHub】

【2017.05.07】

【台大李宏毅中文深度学习课程(2017)】”NTUEE Machine Learning and having it Deep and Structured(MLDS) (2017)”

【paper】【GitHub】【video】

TensorFlow教程

【paper】【GitHub】

【2017.05.08】

【Keras教程：Python深度学习】《Keras Tutorial: Deep Learning in Python》by Karlijn Willems

【paper】【GitHub】

【2017.05.09】

【用Anaconda玩转深度学习】《Deep Learning with Anaconda(AnacondaCON 2017) - YouTube》by Stan Seibert & Matt Rocklin

【paper】【GitHub】【video】

5.2 其它

【2017.04.23】

哥伦比亚大学与Adobe提出新方法，可将随机梯度下降用作近似贝叶斯推理

【paper】【GitHub】

英特尔深度学习产品综述：如何占领人工智能市场

【paper】【GitHub】

【2017.04.24】

28款GitHub最流行的开源机器学习项目：TensorFlow排榜首

【paper】【GitHub】

【2017.04.26】

英国皇家学会百页报告：机器学习的力量与希望（豪华阵容参与完成）

【paper】【GitHub】

深度学习在推荐算法上的应用进展

【paper】【GitHub】

周志华教授gcForest（多粒度级联森林）算法预测股指期货涨跌

【paper】【GitHub】

【2017.05.07】

市值250亿的特征向量——谷歌背后的线性代数

【paper】【GitHub】

【可重现/易分享数据科学项目框架】’DVC - Data Version Control: Make your data science projects reproducible and shareable

【paper】【GitHub】

《Fast k-means based on KNN Graph》C Deng, W Zhao [Xiamen University] (2017)

【paper】【GitHub】

【信息检索人工神经网络模型】《Neural Models for Information Retrieval》B Mitra, N Craswell [Microsoft] (2017)

【paper】【GitHub】

地平线机器人杨铭：深度神经网络在图像识别应用中的演化

【paper】【GitHub】

【(Python)Facebook的开源AI对话研究框架】’ParlAI - A framework for training and evaluating AI models on a variety of openly available dialog datasets.’

【paper】【GitHub】

【(Python)深度神经网络多标签文本分类框架】’magpie - Deep neural network framework for multi-label text classification’ by inspirehep

【paper】【GitHub】

【(300万)Instacart在线杂货购物数据集】《3 Million Instacart Orders, Open Sourced》by Jeremy Stanley

【paper】【GitHub】

【基于语言/网络结构的推荐系统GraphNet】《GraphNet: Recommendation system based on language and network structure》R Ying, Y Li, X Li [Stanford University] (2017)

【paper】【GitHub】

【2017.05.08】

【将Python 3.x代码转换成Python2.x代码的Python-Python编译器】’Py-backwards - Python to python compiler that allows you to use Python 3.6 features in older versions.’ by Vladimir Iakovlev

【paper】【GitHub】

【2017.05.09】

【Xgboost新增GPU加速建树算法】”Xgboost GPU - CUDA Accelerated Tree Construction Algorithm”

【paper】【GitHub】

【独立开发者赚钱资料集锦】’awesome-indie - Resources for independent developers to make money’ by Joan Boixadós

【paper】【GitHub】

【基于MAPD/Anaconda/H2O的GPU数据分析框架】’GPU Data Frame with a corresponding Python API’

【paper】【GitHub】

从文本到视觉：各领域最前沿的论文集合

【paper】【GitHub】

【2017.05.10】

【(C++)信息检索框架库Trinity】’Trinity IR Infrastructure’ by Phaistos Networks GitHub:

【paper】【GitHub】

参考

爱可可-爱生活

机器之心synced

机器学习研究会