機器學習、深度學習、計算機視覺、自然語言處理及應用案例——乾貨分享（持續更新……）
author@jason_ql
http://blog.csdn.net/lql0716
GitChat提問碼：
1、機器學習/深度學習
1.1 對抗生成網絡GAN
【2017.04.21】
【2017.04.22】
【2017.04.23】
- TP-GAN 讓圖像生成再獲突破，根據單一側臉生成正面逼真人臉
【鏈接】【GitHub】
【2017.04.26】
- 【對抗生成網絡GAN教程】
《Tutorial on GANs》by Adit Deshpande
【鏈接】【GitHub】
【2017.05.07】
- 【GAN相關資源與實現】’Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN’ by YadiraF
【鏈接】【GitHub】- 【PyTorch實現的CoGAN】《Coupled Generative Adversarial Networks》M Liu, O Tuzel [Mitsubishi Electric Research Labs (MERL)] (2016)
【鏈接】【GitHub】- 【利用CGAN生成Sketch漫畫】《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)
【鏈接】【GitHub】- 《Adversarial Feature Learning》J Donahue, P Krähenbühl, T Darrell [UC Berkeley]
【鏈接】【GitHub】- 【PyTorch實現的DCGAN、pix2pix、DiscoGAN、CycleGAN、BEGAN VAE、Neural Style Transfer、Char RNN等】’Paper Implementations - Use PyTorch to implement some classic frameworks’ by SunshineAtNoon
【鏈接】【GitHub】- 【GAN畫風遷移】《Generative Adversarial Networks for Style Transfer (LIVE) - YouTube》by Siraj Raval
【鏈接】【GitHub】【video】
【2017.05.08】
- 生成對抗網絡（GAN）研究年度進展評述
【鏈接】【GitHub】- 【對抗生成網絡(Gan)深入研究(文獻/教程/模型/框架/庫等)】《Delving deep into GANs》by Grigorios Kalliatakis
【鏈接】【GitHub】- 【對抗式機器翻譯】《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)
【鏈接】【GitHub】- 【CycleGAN生成模型：熊變熊貓】’Models generated by CycleGAN’ by Tatsuya
【鏈接】【GitHub】- 【對抗生成網絡(GAN)】《Generative Adversarial Networks (LIVE) - YouTube》by Siraj Raval
【鏈接】【GitHub】【video】- 【Keras實現的ACGAN/DCGAN】’Implementation of some basic GAN architectures in Keras’ by Batchu Venkat Vishal
【鏈接】【GitHub】
【2017.05.09】
【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)
【鏈接】【GitHub】【GitHub2】- 《Geometric GAN》J H Lim, J C Ye [ETRI & KAIST] (2017)
【鏈接】【GitHub】- 【PyTorch實現的CycleGAN/SGAN跨域遷移(MNIST-to-SVHN & SVHN-to-MNIST)】’PyTorch Implementation of CycleGAN and SGAN for Domain Transfer (Minimal)’ by yunjey GitHub:
【鏈接】【GitHub】
1.2 神經網絡
【2017.04.24】
- 如何用PyTorch實現遞歸神經網絡？
【鏈接】【GitHub】
【2017.04.25】
- 一個基於TensorFlow的簡單故事生成案例：帶你瞭解LSTM
【鏈接】【GitHub】
【2017.05.07】
- 深度學習10大框架對比分析
【鏈接】【GitHub】- 深度學習之CNN卷積神經網絡
【鏈接】【GitHub】- 【Keras教程：Python深度學習】《Keras Tutorial: Deep Learning in Python》by Karlijn Willems
【鏈接】【GitHub】- TensorFlow 官方解讀：如何在多系統和網絡拓撲中構建高性能模型
【鏈接】【GitHub】- 從自編碼器到生成對抗網絡：一文縱覽無監督學習研究現狀
【鏈接】【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)
【鏈接】【GitHub】
-【基於OpenAI Gym/Tensorflow/Keras的增強學習實驗平臺】’OpenAI Lab - An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.’ by Wah Loon Keng
【鏈接】【GitHub】- 【基於生成卷積網絡的潛在指紋重建】《Generative Convolutional Networks for Latent Fingerprint Reconstruction》J Svoboda, F Monti, M M. Bronstein [USI Lugano] (2017)
【鏈接】【GitHub】- 【TensorFlow入門代碼集錦】’tensorflow-resources - Curated Tensorflow code resources to help you get started’ by Skcript
【鏈接】【GitHub】- 入門級攻略：機器學習 VS. 深度學習
【鏈接】【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)
【鏈接】【GitHub】- TensorFlow基準：圖像分類模型在各大平臺的測試研究
【鏈接】【GitHub】- 谷歌開源深度學習街景文字識別模型：讓地圖隨世界實時更新
【鏈接】【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)
【鏈接】【GitHub】- 【利用強化學習設計神經網絡架構】《Designing Neural Network Architectures using Reinforcement Learning》B Baker, O Gupta, N Naik, R Raskar [MIT] (2016)
【鏈接】【GitHub】- 【神經網絡：三萬英尺高空縱覽入門】《Neural Networks : A 30,000 Feet View for Beginners | Learn OpenCV》by Satya Mallick
【鏈接】【GitHub】- Top100論文導讀：深入理解卷積神經網絡CNN（Part Ⅰ）
【鏈接】【GitHub】- Top100論文導讀：深入理解卷積神經網絡CNN（Part Ⅱ）
【鏈接】【GitHub】
-【深度神經網絡權值初始化的研究】《On weight initialization in deep neural networks》S K Kumar (2017)
【鏈接】【GitHub】
【2017.05.08】
【提升結構化特徵嵌入深度度量學習】《Deep Metric Learning via Lifted Structured Feature Embedding》H Oh Song, Y Xiang, S Jegelka, S Savarese (2016)
【鏈接】【GitHub】【圖的深度特徵學習】《Deep Feature Learning for Graphs》R A. Rossi, R Zhou, N K. Ahmed [Palo Alto Research Center (Xerox
PARC) & Intel Labs] (2017)
【鏈接】【GitHub】- 【用於性能分析、模型優化的神經網絡生成器】’Perceptron - A flexible artificial neural network builder to analysis performance, and optimise the best model.’ by Caspar Wylie
【鏈接】【GitHub】- 【TensorFlow最佳實踐之文件、文件夾與模型架構實用建議】《TensorFlow: A proposal of good practices for files, folders and models architecture》by Morgan
【鏈接】【GitHub】- 【帶有快速局部濾波的圖CNN】《Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering》M Defferrard, X Bresson, P Vandergheynst [EPFL] (2016)
【鏈接】【GitHub】- 【(Tensorflow/TFLearn)RNN命名實體識別】“Named Entity Recognition using Recurrent Neural Networks in Tensorflow and TFLearn” by Dhwaj Raj
【鏈接】【GitHub】【深度學習的侷限性】《Failures of Deep Learning》S Shalev-Shwartz, O Shamir, S Shammah [The Hebrew University & Weizmann Institute] (2017)
【鏈接】【GitHub】【video】【基於矩陣乘法的並行多通道卷積】《Parallel Multi Channel Convolution using General Matrix Multiplication》A Vasudevan, A Anderson, D Gregg [Trinity College Dublin] (2017)
【鏈接】【GitHub】- 【在手機上進行深度學習訓練】《Migrate Deep Learning Training onto Mobile Devices!》by Saman BigManborn
【鏈接】【GitHub】- 【TensorFlow實現的RNN(LSTM)序列預測】’tensorflow-lstm-regression - Sequence prediction using recurrent neural networks(LSTM) with TensorFlow’ by mouradmourafiq
【鏈接】【GitHub】- 【TensorFlow 1.1.0發佈】”TensorFlow 1.1.0 Released”
【鏈接】【GitHub】【CNN到圖結構數據的推廣】《A Generalization of Convolutional Neural Networks to Graph-Structured Data》Y Hechtlinger, P Chakravarti, J Qin [CMU] (2017)
【鏈接】【GitHub】Momenta研發總監任少卿：From Faster R-CNN to Mask R-CNN
【鏈接】【GitHub】《Deep Multitask Learning for Semantic Dependency Parsing》H Peng, S Thomson, N A. Smith [CMU] (2017)
【鏈接】【GitHub】【利用整流單元稀疏性加快卷積神經網絡】《Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units》S Shi, X Chu [Hong Kong Baptist University] (2017)
【鏈接】【GitHub】- 【深度學習之CNN卷積神經網絡】《Deep Learning #2: Convolutional Neural Networks》by Rutger Ruizendaal
【鏈接】【GitHub】- 【PyTorch試煉場：提供各主流預訓練模型】’pytorch-playground - Base pretrained model and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)’ by Aaron Chen
【鏈接】【GitHub】- 從自編碼器到生成對抗網絡：一文縱覽無監督學習研究現狀
【鏈接】【GitHub】
【2017.05.09】
- Learning Deep Learning with Keras
【鏈接】【GitHub】- 【TensorFlow生成模型庫】’A Library for Generative Models’
【鏈接】【GitHub】- 【深度學習的過去、現在和未來】《Deep Learning – Past, Present, and Future》by Henry H. Eckerson
【鏈接】【GitHub】- 正在涌現的新型神經網絡模型：優於生成對抗網絡
【鏈接】【GitHub】- 【貝葉斯深度學習文獻列表】’A curated list of resources dedicated to bayesian deep learning’ by Rabindra Nath Nandi
【鏈接】【GitHub】- 【面向推薦系統的深度學習文獻列表】’Deep-Learning-for-Recommendation-Systems - Deep Learning based articles , paper and repositories for Recommender Systems’ by Rabindra Nath Nandi
【鏈接】【GitHub】
【2017.05.10】
- 【深度學習職位面試經驗分享】《My deep learning job interview experience sharing》by Justin Ho
【鏈接】【GitHub】- 《Convolutional Sequence to Sequence Learning》J Gehring, M Auli, D Grangier, D Yarats, Y N. Dauphin [Facebook AI Research] (2017)
【鏈接】【GitHub】- 【VGG19的TensorFlow實現/詳解】’VGG19_with_tensorflow - An easy implement of VGG19 with tensorflow, which has a detailed explanation.’ by Jipeng Huang
【鏈接】【GitHub】- 【Keras實現的深度聚類】“Keras implementation of Deep Clustering paper” by Eduardo Silva
【鏈接】【GitHub】
1.3 機器學習
【2017.05.07】
- 【無監督學習縱覽】《Navigating the Unsupervised Learning Landscape》by Eugenio Culurciello
【鏈接】【GitHub】- 【(Python)機器學習導論課程資料】’Materials for the “Introduction to Machine Learning” class’ by Andreas Mueller
【鏈接】【GitHub】- 【Newton ADMM快速準平滑牛頓法】’A Newton ADMM based solver for Cone programming.’
【鏈接】【GitHub】- 【超大規模機器學習工具集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’
【鏈接】【GitHub】- 關於遷移學習的一些資料
【鏈接】【GitHub】- 《Clustering with Adaptive Structure Learning: A Kernel Approach》Z Kang, C Peng, Q Cheng [Southern Illinois University] (2017)
【鏈接】【GitHub】- 【(R)稀疏貝葉斯網絡學習】’sparsebn - Software for learning sparse Bayesian networks’ by Bryon Aragam
【鏈接】【GitHub】- 【Node.js機器學習/自然語言處理/情感分析工具包】’salient - Machine Learning, Natural Language Processing and Sentiment Analysis Toolkit for Node.js’ by Thomas Holloway
【鏈接】【GitHub】- Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
【鏈接】【GitHub】- 機器學習中容易犯下的錯
【鏈接】【GitHub】
【2017.05.08】
- 【(C/C++ and MATLAB/Octave)互信息函數工具箱】’MIToolbox - Mutual Information functions for C and MATLAB’ by Adam Pocock
【鏈接】【GitHub】- 【Criteo 1TB數據集上多機器學習算法Benchmark】’Benchmark of different ML algorithms on Criteo 1TB dataset’ by Rambler Digital Solutions
【鏈接】【GitHub】- 機器學習十大常用算法
【鏈接】【GitHub】- 【加速隨機梯度下降】《Accelerating Stochastic Gradient Descent》P Jain, S M. Kakade, R Kidambi, P Netrapalli, A Sidford [Microsoft Research & University of Washington & Stanford University] (2017)
【鏈接】【GitHub】- 【(C++)大規模稀疏矩陣分解包】“LIBMF - library for large-scale sparse matrix factorization” by cjlin1
【鏈接】【GitHub】- 【(C/Python/Matlab)求解大規模正則線性分類與迴歸的簡單包】“LIBLINEAR - simple package for solving large-scale regularized linear classification and regression” by cjlin1
【鏈接】【GitHub】- 【批量歸一化(Batch Norm)概述】《Appendix: A Batch Norm Overview》by alexirpan
【鏈接】【GitHub】
【2017.05.09】
- 譜聚類
【鏈接】【GitHub】
【2017.05.10】
- 【學習非極大值抑制】《Learning non-maximum suppression》J Hosang, R Benenson, B Schiele [Max Planck Institut für Informatik] (2017)
【鏈接】【GitHub】- 【(Python)機器學習工作流框架】’AlphaPy - Machine Learning Pipeline for Python’ by ScottFree Analytics
【鏈接】【GitHub】- 【如何解釋機器學習模型和結果】《Ideas on interpreting machine learning | O’Reilly Media》by Patrick HallWen Phan, SriSatish Ambati
【鏈接】【GitHub】
2、計算機視覺
【2017.04.21】
- OpenCV/Python/dlib人臉關鍵點實時標定
【paper】【GitHub】
【2017.04.22】
- 【高效的卷積神經網絡在手機中的應用】MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
【paper】【GitHub】- 【生成式人臉補全】《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)《譯：多目標追蹤的現狀分析》
【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)《譯：基於PAF的實時二維姿態估計》
【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】- 自動駕駛計算機視覺研究綜述：難題、數據集與前沿成果（附67頁論文下載）
【paper】【GitHub】- 谷歌推出最新“手機版”視覺應用的卷積神經網絡—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】
【單目視頻深度幀間運動估計無監督學習框架】’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)視頻/圖片序列人臉標定】’Find Face Landmarks - C++ \ Matlab library for finding face landmarks and bounding boxes in video\image sequences.’ by Yuval Nirkin
【paper】【GitHub】- 【(Keras)UNET圖像分割】’ZF_UNET_224 Pretrained Model - Modification of convolutional neural net “UNET” for image segmentation in Keras framework’ by ZFTurbo
【paper】【GitHub】【複雜條件下的深度人臉分割】”Deep face segmentation in extremely hard conditions” by Yuval Nirkin
【paper】【GitHub】【基於單目RGB圖像的實時3D人體姿態估計】《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】【衣服檢測與識別】《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 學習與開發經驗分享
【paper】【GitHub】【大規模街道級圖片(分割)數據集】《Releasing the World’s Largest Street-level Imagery Dataset for Teaching Machines to See》by Peter Kontschieder
【paper】【GitHub】【dataset】【基於深度增強學習的交叉路口車輛自動導航】《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】- 十分鐘看懂圖像語義分割技術
【paper】【GitHub】- 【(C++)實時多人關鍵點檢測】’OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library’
【paper】【GitHub】- 計算機視覺、機器學習相關領域論文和源代碼大集合
【paper】【GitHub】- 【(Tensorflow)RPN+人體檢測】’RPNplus - RPN+(Tensorflow) for people detection’ by Shiyu Huang
【paper】【GitHub】- 【(C++/OpenCV3)實時可變人臉追蹤】’Real time deformable face tracking in C++ with OpenCV 3.’ by Kyle McDonald
【paper】【GitHub】【圖片快速標記】《How to Label Images Quickly 》by Pete Warden
【paper】【paper2】【GitHub】【基於深度圖像類比的視覺要素遷移】《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】【基於深度學習的質譜成像中的腫瘤分類】《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手機上基於TensorFlow的人體行爲識別】《Deploying Tensorflow model on Andorid device for Human Activity Recognition》by Aaqib Saeed
【paper】【paper2】【GitHub】- 【TensorFlow圖像自動描述】《Caption this, with TensorFlow | O’Reilly Media》by Raul Puri, Daniel Ricciardelli
【paper】【paper2】【GitHub】- 【基於CNN (InceptionV1) + STFT的Kaggle鯨魚檢測競賽方案】’CNN (InceptionV1) + STFT based Whale Detection Algorithm - A whale detector design for the Kaggle whale-detector challenge!’ by Tarin Ziyaee
【paper】【GitHub】- 【TensorFlow實現的攝像頭pix2pix圖圖轉換】’webcam-pix2pix-Tensorflow - Source code and pretrained model for webcam pix2pix’ by Memo Akten
【paper】【GitHub】- 【圖像分類的大規模進化】《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】
- 人臉檢測與識別的趨勢和分析
【paper】【GitHub】- 【全局/局部一致圖像補全】《Globally and Locally Consistent Image Completion》S Iizuka, E Simo-Serra, H Ishikawa (2017)
【paper】【GitHub】- 【基於CNN的面部表情識別】《Convolutional Neural Networks for Facial Expression Recognition》S Alizadeh, A Fazel [Stanford University] (2017)
【paper】【GitHub】- 計算機視覺識別簡史：從 AlexNet、ResNet 到 Mask RCNN
【paper】【GitHub】- 【臉部識別與聚類】《Face Identification and Clustering》A Dhingra [The State University of New Jersey] (2017)
【paper】【GitHub】- 【(TensorFlow)通用U-Net圖像分割】’Tensorflow Unet - Generic U-Net Tensorflow implementation for image segmentation’ by Joel Akeret
【paper】【GitHub】- 【深度學習介紹之文本圖像生成】《How to Convert Text to Images - Intro to Deep Learning #16 - YouTube》by Siraj Raval
【paper】【GitHub】- 【一個深度神經網絡如何對自動駕駛做端到端的訓練】《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】- 【基於深度卷積網絡的動態場景關節語義與運動分割】《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】- 【高分辨率圖像的實時語義分割】《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】- 【深度學習應用到語義分割的綜述】《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】- 【醫學圖像的深度遷移學習的原理】《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)基於循環一致對抗網絡的非配對圖到圖翻譯】
【paper】【GitHub】【深度網絡光流估計的演化】《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】【基於p-RNN的目標實例標註】《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】- 【人臉的分割、交換與感知】《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】- 【面向視頻運動估計的幾何感知神經網絡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】- 【基於深度自學習的弱監督目標定位】《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】- 【單個圖像的手部關鍵點檢測】《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】【通過觀察目標運動遷移學習特徵】《Learning Features by Watching Objects Move》D Pathak, R Girshick, P Dollár, T Darrell, B Hariharan [Facebook AI Research & UC Berkeley] (2016)
【paper】【GitHub】【面向深度學習訓練的視頻標記工具】’BeaverDam - Video annotation tool for deep learning training labels’ by Anting Shen
【paper】【GitHub】【生成對抗網絡(GAN)圖片編輯】《Photo Editing with Generative Adversarial Networks | Parallel Forall》by Greg Heinrich
【paper】【paper2】【GitHub】解讀Keras在ImageNet中的應用：詳解5種主要的圖像識別模型
【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】- 【結構感知卷積網絡的人體姿態估計】《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】- 【基於神經網絡的魯棒多視角行人跟蹤】《Robust Multi-view Pedestrian Tracking Using Neural Networks》M Z Alom, T M. Taha [University of Dayton] (2017)
【paper】【GitHub】【視頻密集事件描述】”Dense-Captioning Events in Videos”
【paper】【GitHub】【data】【受Siraj Raval深度學習視頻啓發的每週深度學習實踐挑戰】’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】【醫學圖像分割中迭代估計的歸一化輸入】《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實現卷積神經網絡，用於人臉關鍵點識別
【paper】【GitHub】- 【FRCN(faster-rcnn)文字檢測】’Text-Detection-using-py-faster-rcnn-framework’ by jugg1024
【paper】【GitHub】【手機單目視覺狀態估計器】’VINS-Mobile - Monocular Visual-Inertial State Estimator on Mobile Phones’ by HKUST Aerial Robotics Group
【paper】【GitHub】【R-FCN目標檢測】R-FCN: Object Detection via Region-based Fully Convolutional Networks
【paper】【GitHub】行人檢測、跟蹤與檢索領域年度進展報告
【paper】【GitHub】- 【(TensorFlow)點雲(Point Cloud)分類、分割、場景語義理解統一框架PointNet】’PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation’
【paper】【paper2】【GitHub】【GitHub2】- 【深度視頻去模糊】《Deep Video Deblurring》by Shuochen Su(2016)
【paper】【paper2】【GitHub】【video】【中國的Infervision及其肺癌診斷AI工具】《Chinese startup Infervision emerges from stealth with an AI tool for diagnosing lung cancer | TechCrunch》by Jonathan Shieber
【paper】【paper2】【GitHub】【基於醫院大量胸部x射線數據庫的弱監督分類和常見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】目標跟蹤方法的發展概述
【paper】【GitHub】- 【(Caffe)實時交互式圖片自動着色】《Real-Time User-Guided Image Colorization with Learned Deep Priors》[UC Berkeley] (2017)
【paper】【paper2】【GitHub】【video】- 相術的新衣】《Physiognomy’s New Clothes》by Blaise Aguera y Arcas
【paper】【GitHub】
【2017.05.10】
- 快速生成人臉模型
【paper】【paper2】【GitHub（預計八月開源）】- VALSE2017系列之二: 邊緣檢測領域年度進展報告
【paper】【GitHub】- 【(GTC2017)Stanford發佈0.5PB大規模放射醫療圖像ImageNet數據集】“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】- 【醫療圖像深度學習】《Medical Image Analysis with Deep Learning》by Taposh Dutta-Roy
Part1
Part2
Part3- 【激光雷達(LIDAR)：自駕車關鍵傳感器】《An Introduction to LIDAR: The Key Self-Driving Car Sensor》by Oliver Cameron
【paper】【GitHub】- 【根據目標臉生成帶語音的視頻】《You said that?》J S Chung, A Jamaludin, A Zisserman [University of Oxford] (2017)
【paper】【GitHub】- 【用於圖像生成和數據增強的生成協作網】《Generative Cooperative Net for Image Generation and Data Augmentation》Q Xu, Z Qin, T Wan [Beihang University & Alibaba Group] (2017)
【paper】【GitHub】- 【COCO像素級標註數據集】’The official homepage of the COCO-Stuff dataset.’
【paper】【GitHub】- 《COCO-Stuff: Thing and Stuff Classes in Context》 (2017) 【paper】【GitHub】
- 【LinkNet：基於編碼器表示的高效語義分割】《(LinkNet)Feature Forwarding: Exploiting Encoder Representations for Efficient Semantic Segmentation》A Chaurasia, E Culurciello
【paper】【GitHub】【GitHub2】
3、自然語言處理
【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】
- 【對話語料集】’chat corpus collection from various open sources’ by Marsan-Ma
【paper】【GitHub】
【2017.05.07】
- 【從文本中提取特徵的神經網絡技術綜述】《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實踐：序列到序列Attention法-英翻譯】《Practical PyTorch: Translation with a Sequence to Sequence Network and Attention》by Sean Robertson
【paper】【GitHub】- 【PyTorch實踐：探索GloVe詞向量】《Practical PyTorch: Exploring Word Vectors with GloVe》by Sean Robertson
【paper】【GitHub】- 【自然語言生成(NLG)系統評價指標】《How to do an NLG Evaluation: Metrics》by Ehud Reiter
【paper】【paper2】【GitHub】- 【看似靠譜的文本分類對抗樣本】’textfool - Plausible looking adversarial examples for text classification’ by Bogdan Kulynych >【paper】【GitHub】
- 【基於bidirectional GRU-CRF的聯合中文分詞與詞性標註】’A Joint Chinese segmentation and POS tagger based on bidirectional GRU-CRF’ by yanshao9798
【paper】【GitHub】- 【自然語言處理(NLP)入門指南】《How to get started in NLP》by Melanie Tosik
【paper】【GitHub】
【2017.05.08】
- 【(TensorFlow)面向文本相似度檢測的Deep LSTM siamese網絡】’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語種檢測】《Deep Learning: Language identification using Keras & TensorFlow》by Lucas KM
【paper】【GitHub】
-【(C++)神經網絡語種檢測工具】“Compact Language Detector v3 (CLD3) - neural network model for language identification” by Google
【paper】【GitHub】- 【用於文本分類的端到端多視圖網絡】《End-to-End Multi-View Networks for Text Classification》H Guo, C Cherry, J Su [National Research Council Canada] (2017)
【paper】【GitHub】- 【理解非結構化文本數據】《Making Sense of Unstructured Text Data》L Li, W M. Campbell, C Dagli, J P. Campbell [MIT Lincoln Laboratory] (2017)
【paper】【GitHub】- 【非本族語者英語寫作風格檢測】《Detecting English Writing Styles For Non Native Speakers》Y Chen, R Al-Rfou’, Y Choi [Stony Brook University] (2017)
【paper】【GitHub】
【2017.05.10】
4、應用案例
【2017.04.21】
- 深度學習入門實戰（一）-像Prisma一樣算法生成梵高風格畫像
【paper】【GitHub】
【2017.04.22】
- 我們教電腦識別視頻字幕
【paper】【GitHub】
【2017.04.24】
【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】
- 【假新聞的實時檢測】《How to Detect Fake News in Real-Time 》by Krishna Bharat
【paper】【GitHub】
5、綜合
5.1 教程
【2017.04.21】
- 30 Free Courses: Neural Networks, Machine Learning, Algorithms, AI
【paper】【GitHub】
【2017.04.22】
【2017.04.23】
- 機器學習(Machine Learning)&深度學習(Deep Learning)資料(Chapter 1)
【paper】【GitHub】
【2017.05.07】
【2017.05.08】
- 【Keras教程：Python深度學習】《Keras Tutorial: Deep Learning in Python》by Karlijn Willems
【paper】【GitHub】
【2017.05.09】
- 【用Anaconda玩轉深度學習】《Deep Learning with Anaconda(AnacondaCON 2017) - YouTube》by Stan Seibert & Matt Rocklin
【paper】【GitHub】【video】
5.2 其它
【2017.04.23】
【2017.04.24】
- 28款GitHub最流行的開源機器學習項目：TensorFlow排榜首
【paper】【GitHub】
【2017.04.26】
【2017.05.07】
- 市值250億的特徵向量——谷歌背後的線性代數
【paper】【GitHub】- 【可重現/易分享數據科學項目框架】’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】- 【信息檢索人工神經網絡模型】《Neural Models for Information Retrieval》B Mitra, N Craswell [Microsoft] (2017)
【paper】【GitHub】- 地平線機器人楊銘：深度神經網絡在圖像識別應用中的演化
【paper】【GitHub】- 【(Python)Facebook的開源AI對話研究框架】’ParlAI - A framework for training and evaluating AI models on a variety of openly available dialog datasets.’
【paper】【GitHub】- 【(Python)深度神經網絡多標籤文本分類框架】’magpie - Deep neural network framework for multi-label text classification’ by inspirehep
【paper】【GitHub】- 【(300萬)Instacart在線雜貨購物數據集】《3 Million Instacart Orders, Open Sourced》by Jeremy Stanley
【paper】【GitHub】- 【基於語言/網絡結構的推薦系統GraphNet】《GraphNet: Recommendation system based on language and network structure》R Ying, Y Li, X Li [Stanford University] (2017)
【paper】【GitHub】
【2017.05.08】
- 【將Python 3.x代碼轉換成Python2.x代碼的Python-Python編譯器】’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加速建樹算法】”Xgboost GPU - CUDA Accelerated Tree Construction Algorithm”
【paper】【GitHub】- 【獨立開發者賺錢資料集錦】’awesome-indie - Resources for independent developers to make money’ by Joan Boixadós
【paper】【GitHub】- 【基於MAPD/Anaconda/H2O的GPU數據分析框架】’GPU Data Frame with a corresponding Python API’
【paper】【GitHub】- 從文本到視覺：各領域最前沿的論文集合
【paper】【GitHub】
【2017.05.10】
- 【(C++)信息檢索框架庫Trinity】’Trinity IR Infrastructure’ by Phaistos Networks GitHub:
【paper】【GitHub】