CV领域
1.进展：如上图所述，当前CV领域主要包括两个大的方向，”低层次的感知” 和 “高层次的认知”。
2.主要的应用领域：视频监控、人脸识别、医学图像分析、自动驾驶、 机器人、AR、VR
3.主要的技术：分类、目标检测（识别)、分割、目标追踪、边缘检测、姿势评估、理解CNN、超分辨率重建、序列学习、特征检测与匹配、图像标定，视频标定、问答系统、图片生成（文本生成图像）、视觉关注性和显著性（质量评价）、人脸识别、3D重建、推荐系统、细粒度图像分析、图像压缩
分类主要需要解决的问题是“我是谁？” 目标检测主要需要解决的问题是“我是谁？ 我在哪里？” 分割主要需要解决的问题是“我是谁？ 我在哪里？你是否能够正确分割我？” 目标追踪主要需要解决的问题是“你能不能跟上我的步伐，尽快找到我？” 边缘检测主要需要解决的问题是：“如何准确的检测到目标的边缘？” 人体姿势评估主要需要解决的问题是：“你需要通过我的姿势判断我在干什么？” 理解CNN主要需要解决的问题是：“从理论上深层次的去理解CNN的原理？” 超分辨率重建主要需要解决的问题是：“你如何从低质量图片获得高质量的图片？” 序列学习主要解决的问题是“你知道我的下一幅图像或者下一帧视频是什么吗？” 特征检测与匹配主要需要解决的问题是“检测图像的特征，判断相似程度？” 图像标定主要需要解决的问题是“你能说出图像中有什么东西？他们在干什么呢？” 视频标定主要需要解决的问题是“你知道我这几帧视频说明了什么吗？” 问答系统主要需要解决的问题是：“你能根据图像正确回答我提问的问题吗？” 图片生成主要需要解决的问题是：“我能通过你给的信息准确的生成对应的图片？” 视觉关注性和显著性主要需要解决的问题是：“如何提出模拟人类视觉注意机制的模型？” 人脸识别主要需要解决的问题是：“机器如何准确的识别出同一个人在不同情况下的脸？” 3D重建主要需要解决的问题是“你能通过我给你的图片生成对应的高质量3D点云吗？” 推荐系统主要需要解决的问题是“你能根据我的输入给出准确的输出吗？” 细粒度图像分析主要需要解决的问题是“你能辨别出我是哪一种狗吗？等这些更精细的任务” 图像压缩主要需要解决的问题是“如何以较少的比特有损或者无损的表示原来的图像？”
注： 1. 以下我主要从CV领域中的各个小的领域入手，总结该领域中一些网络模型，基本上覆盖到了各个领域，力求完整的收集各种经典的模型，顺序基本上是按照时间的先后，一般最后是该领域最新提出来的方案，我主要的目的是做一个整理，方便自己和他人的使用，你不再需要去网上收集大把的资料，需要的是仔细分析这些模型，并提出自己新的模型。这里面收集的论文质量都比较高，主要来自于ECCV、ICCV、CVPR、PAM、arxiv、ICLR、ACM等顶尖国际会议。并且为每篇论文都添加了链接。可以大大地节约你的时间。同时，我挑选出论文比较重要的网络模型或者整体架构，可以方便你去进行对比。有一个更好的全局观。具体 细节需要你去仔细的阅读论文。由于个人的精力有限，我只能做成这样，希望大家能够理解。谢谢。 2. 我会利用自己的业余时间来更新新的模型，但是由于时间和精力有限，可能并不完整，我希望大家都能贡献的一份力量，如果你发现新的模型，可以联系我，我会及时回复大家，期待着的加入，让我们一起服务大家！
如下图所示：
分类：这是一个基础的研究课题，已经获得了很高的准确率，在一些场合上面已经远远地超过啦人类啦！
典型的网络模型
LeNet http://yann.lecun.com/exdb/lenet/index.html
AlexNet http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification https://arxiv.org/pdf/1502.01852.pdf
Batch Normalization https://arxiv.org/pdf/1502.03167.pdf
GoogLeNet http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
VGGNet https://arxiv.org/pdf/1409.1556.pdf
ResNet https://arxiv.org/pdf/1512.03385.pdf
InceptionV4（Inception-ResNet） https://arxiv.org/pdf/1602.07261.pdf
LeNet网络1：
LeNet网络2：
AlexNet网络1：
AlexNet网络2：
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification网络：
GoogLeNet网络1：
GoogLeNet网络2：
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification网络：
Batch Normalization： VGGNet网络1：
VGGNet网络2：
ResNet网络：
InceptionV4网络：
图像检测：这是基于图像分类的基础上所做的一些研究，即分类+定位。
典型网络
OVerfeat https://arxiv.org/pdf/1312.6229.pdf
RNN https://arxiv.org/pdf/1311.2524.pdf
SPP-Net https://arxiv.org/pdf/1406.4729.pdf
DeepID-Net https://arxiv.org/pdf/1409.3505.pdf
Fast R-CNN https://arxiv.org/pdf/1504.08083.pdf
R-CNN minus R https://arxiv.org/pdf/1506.06981.pdf
End-to-end people detection in crowded scenes https://arxiv.org/pdf/1506.04878.pdf
DeepBox https://arxiv.org/pdf/1505.02146.pdf
MR-CNN http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf
Faster R-CNN https://arxiv.org/pdf/1506.01497.pdf
YOLO https://arxiv.org/pdf/1506.02640.pdf
DenseBox https://arxiv.org/pdf/1509.04874.pdf
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning https://arxiv.org/pdf/1503.00949.pdf
R-FCN https://arxiv.org/pdf/1605.06409.pdf
SSD https://arxiv.org/pdf/1512.02325v2.pdf
Inside-Outside Net https://arxiv.org/pdf/1512.04143.pdf
G-CNN https://arxiv.org/pdf/1512.07729.pdf
PVANET https://arxiv.org/pdf/1608.08021.pdf
Speed/accuracy trade-offs for modern convolutional object detectors https://arxiv.org/pdf/1611.10012v1.pdf
OVerfeat网络：
R-CNN网络：
SPP-Net网络：
DeepID-Net网络：
DeepBox网络：
MR-CNN网络：
Fast-RCNN网络：
R-CNN minus R网络：
End-to-end people detection in crowded scenes网络：
Faster-RCNN网络：
DenseBox网络：
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning网络：
R-FCN网络：
YOLO和SDD网络：
Inside-Outside Net网络：
G-CNN网络：
PVANET网络：
Speed/accuracy trade-offs for modern convolutional object detectors：
图像分割
经典网络模型：
FCN https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
segNet https://arxiv.org/pdf/1511.00561.pdf
Deeplab https://arxiv.org/pdf/1606.00915.pdf
deconvNet https://arxiv.org/pdf/1505.04366.pdf
Conditional Random Fields as Recurrent Neural Networks http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf
Semantic Segmentation using Adversarial Networks https://arxiv.org/pdf/1611.08408.pdf
SEC: Seed, Expand and Constrain： http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf
Efficient piecewise training of deep structured models for semantic segmentation https://arxiv.org/pdf/1504.01013.pdf
Semantic Image Segmentation via Deep Parsing Network https://arxiv.org/pdf/1509.02634.pdf
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation https://arxiv.org/pdf/1503.01640.pdf
Learning Deconvolution Network for Semantic Segmentation https://arxiv.org/pdf/1505.04366.pdf
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation https://arxiv.org/pdf/1506.04924.pdf
PUSHING THE BOUNDARIES OF BOUNDARY DETECTION USING DEEP LEARNING https://arxiv.org/pdf/1511.07386.pdf
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network https://arxiv.org/pdf/1512.07928.pdf
Feedforward Semantic Segmentation With Zoom-Out Features http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf
Joint Calibration for Semantic Segmentation https://arxiv.org/pdf/1507.01581.pdf
Hypercolumns for Object Segmentation and Fine-Grained Localization http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf
Scene Parsing with Multiscale Feature Learning http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf
Learning Hierarchical Features for Scene Labeling http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS https://arxiv.org/pdf/1511.07122v2.pdf
Weakly supervised graph based semantic segmentation by learning communities of image-parts http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf
FCN网络1：
FCN网络2：
segNet网络：
Deeplab网络：
deconvNet网络：
Conditional Random Fields as Recurrent Neural Networks网络：
Semantic Segmentation using Adversarial Networks网络：
SEC: Seed, Expand and Constrain网络：
Efficient piecewise training of deep structured models for semantic segmentation网络：
Semantic Image Segmentation via Deep Parsing Network网络：
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation：
Learning Deconvolution Network for Semantic Segmentation：
PUSHING THE BOUNDARIES OF BOUNDARY DETECTION USING DEEP LEARNING：
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation：
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network：
Feedforward Semantic Segmentation With Zoom-Out Features网络：
Joint Calibration for Semantic Segmentation：
Hypercolumns for Object Segmentation and Fine-Grained Localization：
Learning Hierarchical Features for Scene Labeling：
MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS：
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing：
Weakly supervised graph based semantic segmentation by learning communities of image-parts：
Scene Parsing with Multiscale Feature Learning：
目标追踪
经典网络：
DLT https://pdfs.semanticscholar.org/b218/0fc4f5cb46b5b5394487842399c501381d67.pdf
Transferring Rich Feature Hierarchies for Robust Visual Tracking https://arxiv.org/pdf/1501.04587.pdf
FCNT http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf
Hierarchical Convolutional Features for Visual Tracking http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf
MDNet https://arxiv.org/pdf/1510.07945.pdf
Recurrently Target-Attending Tracking http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.pdf
DeepTracking http://www.bmva.org/bmvc/2014/files/paper028.pdf
DeepTrack http://www.bmva.org/bmvc/2014/files/paper028.pdf
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network https://arxiv.org/pdf/1502.06796.pdf
Transferring Rich Feature Hierarchies for Robust Visual Tracking https://arxiv.org/pdf/1501.04587.pdf
DLT网络：
Transferring Rich Feature Hierarchies for Robust Visual Tracking网络：
FCNT网络：
Hierarchical Convolutional Features for Visual Tracking网络：
MDNet网络：
DeepTracking网络：
ecurrently Target-Attending Tracking网络：
DeepTrack网络：
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network：
边缘检测
经典模型：
HED https://arxiv.org/pdf/1504.06375.pdf
DeepEdge https://arxiv.org/pdf/1412.1123.pdf
DeepConto http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf
HED网络：
DeepEdge网络：
DeepContour网络：
人体姿势评估
经典模型：
DeepPose https://arxiv.org/pdf/1312.4659.pdf
JTCN https://www.robots.ox.ac.uk/~vgg/rg/papers/tompson2014.pdf
Flowing convnets for human pose estimation in videos https://arxiv.org/pdf/1506.02897.pdf
Stacked hourglass networks for human pose estimation https://arxiv.org/pdf/1603.06937.pdf
Convolutional pose machines https://arxiv.org/pdf/1602.00134.pdf
Deepcut https://arxiv.org/pdf/1605.03170.pdf
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields https://arxiv.org/pdf/1611.08050.pdf
DeepPose网络：
JTCN网络：
Flowing convnets for human pose estimation in videos网络：
Stacked hourglass networks for human pose estimation网络：
Convolutional pose machines网络：
Deepcut网络：
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields网络：
理解CNN
经典网络：
Visualizing and Understanding Convolutional Networks https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf
Inverting Visual Representations with Convolutional Networks https://arxiv.org/pdf/1506.02753.pdf
Object Detectors Emerge in Deep Scene CNNs https://arxiv.org/pdf/1412.6856.pdf
Understanding Deep Image Representations by Inverting Them http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf
Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf
Understanding image representations by measuring their equivariance and equivalence http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf
Visualizing and Understanding Convolutional Networks网络：
Inverting Visual Representations with Convolutional Networks：
Object Detectors Emerge in Deep Scene CNNs：
Understanding Deep Image Representations by Inverting Them：
Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images：
Understanding image representations by measuring their equivariance and equivalence：
超分辨率重建
经典模型：
Learning Iterative Image Reconstruction http://www.ais.uni-bonn.de/behnke/papers/ijcai01.pdf
Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid http://www.ais.uni-bonn.de/behnke/papers/ijcia01.pdf
Learning a Deep Convolutional Network for Image Super-Resolution http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf
Image Super-Resolution Using Deep Convolutional Networks https://arxiv.org/pdf/1501.00092.pdf
Accurate Image Super-Resolution Using Very Deep Convolutional Networks https://arxiv.org/pdf/1511.04587.pdf
Deeply-Recursive Convolutional Network for Image Super-Resolution https://arxiv.org/pdf/1511.04491.pdf
Deep Networks for Image Super-Resolution with Sparse Prior http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf
Perceptual Losses for Real-Time Style Transfer and Super-Resolution https://arxiv.org/pdf/1603.08155.pdf
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network https://arxiv.org/pdf/1609.04802v3.pdf
Learning Iterative Image Reconstruction网络：
Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid：
Learning a Deep Convolutional Network for Image Super-Resolution：
Image Super-Resolution Using Deep Convolutional Networks：
Accurate Image Super-Resolution Using Very Deep Convolutional Networks：
Deeply-Recursive Convolutional Network for Image Super-Resolution：
Deep Networks for Image Super-Resolution with Sparse Prior：
Perceptual Losses for Real-Time Style Transfer and Super-Resolution：
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network：
图像标定
经典模型：
Explain Images with Multimodal Recurrent Neural Networks https://arxiv.org/pdf/1410.1090.pdf
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models https://arxiv.org/pdf/1411.2539.pdf
Long-term Recurrent Convolutional Networks for Visual Recognition and Description https://arxiv.org/pdf/1411.4389.pdf
A Neural Image Caption Generator https://arxiv.org/pdf/1411.4555.pdf
Deep Visual-Semantic Alignments for Generating Image Description http://cs.stanford.edu/people/karpathy/cvpr2015.pdf
Translating Videos to Natural Language Using Deep Recurrent Neural Networks https://arxiv.org/pdf/1412.4729.pdf
Learning a Recurrent Visual Representation for Image Caption Generation https://arxiv.org/pdf/1411.5654.pdf
From Captions to Visual Concepts and Back https://arxiv.org/pdf/1411.4952.pdf
Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf
Phrase-based Image Captioning https://arxiv.org/pdf/1502.03671.pdf
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images https://arxiv.org/pdf/1504.06692.pdf
Exploring Nearest Neighbor Approaches for Image Captioning https://arxiv.org/pdf/1505.04467.pdf
Image Captioning with an Intermediate Attributes Layer https://arxiv.org/pdf/1506.01144.pdf
Learning language through pictures https://arxiv.org/pdf/1506.03694.pdf
Describing Multimedia Content using Attention-based Encoder-Decoder Networks https://arxiv.org/pdf/1507.01053.pdf
Image Representations and New Domains in Neural Image Captioning https://arxiv.org/pdf/1508.02091.pdf
Learning Query and Image Similarities with Ranking Canonical Correlation Analysis http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yao_Learning_Query_and_ICCV_2015_paper.pdf
Generative Adversarial Text to Image Synthesis https://arxiv.org/pdf/1605.05396.pdf
GENERATING IMAGES FROM CAPTIONS WITH ATTENTION https://arxiv.org/pdf/1511.02793.pdf
Explain Images with Multimodal Recurrent Neural Networks：
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models：
Long-term Recurrent Convolutional Networks for Visual Recognition and Description：
A Neural Image Caption Generator：
Deep Visual-Semantic Alignments for Generating Image Description：
Translating Videos to Natural Language Using Deep Recurrent Neural Networks：
Learning a Recurrent Visual Representation for Image Caption Generation：
From Captions to Visual Concepts and Back：
Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention：
Phrase-based Image Captioning：
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images：
Exploring Nearest Neighbor Approaches for Image Captioning：
Image Captioning with an Intermediate Attributes Layer：
Learning language through pictures：
Describing Multimedia Content using Attention-based Encoder-Decoder Networks：
Image Representations and New Domains in Neural Image Captioning：
Learning Query and Image Similarities with Ranking Canonical Correlation Analysis：
Generative Adversarial Text to Image Synthesis：
GENERATING IMAGES FROM CAPTIONS WITH ATTENTION：
视频标注
经典模型：
Long-term Recurrent Convolutional Networks for Visual Recognition and Description https://arxiv.org/pdf/1411.4389.pdf
Translating Videos to Natural Language Using Deep Recurrent Neural Networks https://arxiv.org/pdf/1412.4729.pdf
Joint Modeling Embedding and Translation to Bridge Video and Language https://arxiv.org/pdf/1505.01861.pdf
Sequence to Sequence–Video to Text https://arxiv.org/pdf/1505.00487.pdf
Describing Videos by Exploiting Temporal Structure https://arxiv.org/pdf/1502.08029.pdf
The Long-Short Story of Movie Description https://arxiv.org/pdf/1506.01698.pdf
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books https://arxiv.org/pdf/1506.06724.pdf
Describing Multimedia Content using Attention-based Encoder-Decoder Networks https://arxiv.org/pdf/1507.01053.pdf
Temporal Tessellation for Video Annotation and Summarization https://arxiv.org/pdf/1612.06950.pdf
Summarization-based Video Caption via Deep Neural Networks acm=1492135731_7c7cb5d6bf7455db7f4aa75b341d1a78”>http://delivery.acm.org/10.1145/2810000/2806314/p1191-li.pdf?ip=123.138.79.12&id=2806314&acc=ACTIVE SERVICE&key=BF85BBA5741FDC6E.B37B3B2DF215A17D.4D4702B0C3E38B35.4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492135731_7c7cb5d6bf7455db7f4aa75b341d1a78
Deep Learning for Video Classification and Captioning https://arxiv.org/pdf/1609.06782.pdf
Long-term Recurrent Convolutional Networks for Visual Recognition and Description：
Translating Videos to Natural Language Using Deep Recurrent Neural Networks：
Joint Modeling Embedding and Translation to Bridge Video and Language：
Sequence to Sequence–Video to Text：
Describing Videos by Exploiting Temporal Structure:
The Long-Short Story of Movie Description：
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books：
Describing Multimedia Content using Attention-based Encoder-Decoder Networks：
Temporal Tessellation for Video Annotation and Summarization：
Summarization-based Video Caption via Deep Neural Networks：
Deep Learning for Video Classification and Captioning：
问答系统
经典模型：
VQA: Visual Question Answering https://arxiv.org/pdf/1505.00468.pdf
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images https://arxiv.org/pdf/1505.01121.pdf
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset https://arxiv.org/pdf/1505.02074.pdf
Stacked Attention Networks for Image Question Answering https://arxiv.org/pdf/1511.02274v2.pdf
Dataset and Methods for Multilingual Image Question Answering https://arxiv.org/pdf/1505.05612.pdf
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Dynamic Memory Networks for Visual and Textual Question Answering https://arxiv.org/pdf/1603.01417v1.pdf
Multimodal Residual Learning for Visual QA https://arxiv.org/pdf/1606.01455.pdf
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding https://arxiv.org/pdf/1606.01847.pdf
Training Recurrent Answering Units with Joint Loss Minimization for VQA https://arxiv.org/pdf/1606.03647.pdf
Hadamard Product for Low-rank Bilinear Pooling https://arxiv.org/pdf/1610.04325.pdf
Question Answering Using Deep Learning https://cs224d.stanford.edu/reports/StrohMathur.pdf
VQA: Visual Question Answering：
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images：
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset：
Stacked Attention Networks for Image Question Answering：
Dataset and Methods for Multilingual Image Question Answering：
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction：
Dynamic Memory Networks for Visual and Textual Question Answering：
Multimodal Residual Learning for Visual QA：
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding：
Training Recurrent Answering Units with Joint Loss Minimization for VQA：
Hadamard Product for Low-rank Bilinear Pooling：
Question Answering Using Deep Learning：
图片生成（CNN、RNN、LSTM、GAN）
经典模型：
Conditional Image Generation with PixelCNN Decoders https://arxiv.org/pdf/1606.05328v2.pdf
Learning to Generate Chairs with Convolutional Neural Networks http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf
DRAW: A Recurrent Neural Network For Image Generation https://arxiv.org/pdf/1502.04623v2.pdf
Generative Adversarial Networks https://arxiv.org/pdf/1406.2661.pdf
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks https://arxiv.org/pdf/1506.05751.pdf
A note on the evaluation of generative models https://arxiv.org/pdf/1511.01844.pdf
Variationally Auto-Encoded Deep Gaussian Processes https://arxiv.org/pdf/1511.06455v2.pdf
Generating Images from Captions with Attention https://arxiv.org/pdf/1511.02793v2.pdf
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks https://arxiv.org/pdf/1511.06390v1.pdf
Censoring Representations with an Adversary https://arxiv.org/pdf/1511.05897v3.pdf
Distributional Smoothing with Virtual Adversarial Training https://arxiv.org/pdf/1507.00677v8.pdf
Generative Visual Manipulation on the Natural Image Manifold https://arxiv.org/pdf/1609.03552v2.pdf
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks https://arxiv.org/pdf/1511.06434.pdf
Wasserstein GAN https://arxiv.org/pdf/1701.07875.pdf
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities https://arxiv.org/pdf/1701.06264.pdf
Conditional Generative Adversarial Nets https://arxiv.org/pdf/1411.1784.pdf
InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial Nets https://arxiv.org/pdf/1606.03657.pdf
Conditional Image Synthesis With Auxiliary Classifier GANs https://arxiv.org/pdf/1610.09585.pdf
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient https://arxiv.org/pdf/1609.05473.pdf
Improved Training of Wasserstein GANs https://arxiv.org/pdf/1704.00028.pdf
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis https://arxiv.org/pdf/1704.04086.pdf
Conditional Image Generation with PixelCNN Decoders：
Learning to Generate Chairs with Convolutional Neural Networks：
DRAW: A Recurrent Neural Network For Image Generation：
Generative Adversarial Networks：
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks：
A note on the evaluation of generative models：
Variationally Auto-Encoded Deep Gaussian Processes：
Generating Images from Captions with Attention：
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks：
Censoring Representations with an Adversary：
Distributional Smoothing with Virtual Adversarial Training：
Generative Visual Manipulation on the Natural Image Manifold：
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks：
Wasserstein GAN：
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities：
Conditional Generative Adversarial Nets：
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets：
Conditional Image Synthesis With Auxiliary Classifier GANs：
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient：
Improved Training of Wasserstein GANs：
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis：
视觉关注性和显著性
经典模型：
Predicting Eye Fixations using Convolutional Neural Networks http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf
Learning a Sequential Search for Landmarks http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf
Multiple Object Recognition with Visual Attention https://arxiv.org/pdf/1412.7755.pdf
Recurrent Models of Visual Attention http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf
Capacity Visual Attention Networks http://easychair.org/publications/download/Capacity_Visual_Attention_Networks
Fully Convolutional Attention Networks for Fine-Grained Recognition https://arxiv.org/pdf/1603.06765.pdf
Predicting Eye Fixations using Convolutional Neural Networks：
Learning a Sequential Search for Landmarks：
Multiple Object Recognition with Visual Attention：
Recurrent Models of Visual Attention：
Capacity Visual Attention Networks：
Fully Convolutional Attention Networks for Fine-Grained Recognition：
特征检测与匹配（块）
经典模型：
TILDE: A Temporally Invariant Learned DEtector https://arxiv.org/pdf/1411.4568.pdf
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching https://pdfs.semanticscholar.org/81b9/24da33b9500a2477532fd53f01df00113972.pdf
Discriminative Learning of Deep Convolutional Feature Point Descriptors http://cvlabwww.epfl.ch/~trulls/pdf/iccv-2015-deepdesc.pdf
Learning to Assign Orientations to Feature Points https://arxiv.org/pdf/1511.04273.pdf
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors https://arxiv.org/pdf/1601.05030.pdf
Multi-scale Pyramid Pooling for Deep Convolutional Representation http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7301274
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition https://arxiv.org/pdf/1406.4729.pdf
Learning to Compare Image Patches via Convolutional Neural Networks https://arxiv.org/pdf/1504.03641.pdf
PixelNet: Representation of the pixels, by the pixels, and for the pixels http://www.cs.cmu.edu/~aayushb/pixelNet/pixelnet.pdf
LIFT: Learned Invariant Feature Transform https://arxiv.org/pdf/1603.09114.pdf
TILDE: A Temporally Invariant Learned DEtector：
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching：
Discriminative Learning of Deep Convolutional Feature Point Descriptors：
Learning to Assign Orientations to Feature Points：
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors：
Multi-scale Pyramid Pooling for Deep Convolutional Representation：
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition：
Learning to Compare Image Patches via Convolutional Neural Networks：
PixelNet: Representation of the pixels, by the pixels, and for the pixels：
LIFT: Learned Invariant Feature Transform：
人脸识别
经典模型：
Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks http://vis-www.cs.umass.edu/papers/HuangCVPR12.pdf
Deep Convolutional Network Cascade for Facial Point Detection http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf
Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification acm=1492152722_04e9cce5378080a18ec7e700dfb4cd28”>http://delivery.acm.org/10.1145/2400000/2396303/p749-cai.pdf?ip=123.138.79.12&id=2396303&acc=ACTIVE SERVICE&key=BF85BBA5741FDC6E.B37B3B2DF215A17D.4D4702B0C3E38B35.4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492152722_04e9cce5378080a18ec7e700dfb4cd28
DeepFace: Closing the Gap to Human-Level Performance in Face Verification https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
Deep learning face representation by joint identification-verification https://arxiv.org/pdf/1406.4773.pdf
Deep learning face representation from predicting 10,000 classes http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
Deeply learned face representations are sparse, selective, and robust https://arxiv.org/pdf/1412.1265.pdf
Deepid3: Face recognition with very deep neural networks https://arxiv.org/pdf/1502.00873.pdf
FaceNet: A Unified Embedding for Face Recognition and Clustering https://arxiv.org/pdf/1503.03832.pdf
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness https://arxiv.org/pdf/1609.07304.pdf
Large-pose Face Alignment via CNN-based Dense 3D Model Fitting http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Jourabloo_Large-Pose_Face_Alignment_CVPR_2016_paper.pdf
Unconstrained 3D face reconstruction http://cvlab.cse.msu.edu/pdfs/Roth_Tong_Liu_CVPR2015.pdf
Adaptive contour fitting for pose-invariant 3D face shape reconstruction http://akme-a2.iosb.fraunhofer.de/ETGS15p/2015_Adaptive contour fitting for pose-invariant 3D face shape reconstruction.pdf
High-fidelity pose and expression normalization for face recognition in the wild http://www.cbsr.ia.ac.cn/users/xiangyuzhu/papers/CVPR2015_High-Fidelity.pdf
Adaptive 3D face reconstruction from unconstrained photo collections http://cvlab.cse.msu.edu/pdfs/Roth_Tong_Liu_CVPR16.pdf
Dense 3D face alignment from 2d videos in real-time http://ieeexplore.ieee.org/stamp/stamp.jsp arnumber=7163142
Robust facial landmark detection under significant head poses and occlusion http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wu_Robust_Facial_Landmark_ICCV_2015_paper.pdf
A convolutional neural network cascade for face detection http://users.eecs.northwestern.edu/~xsh835/assets/cvpr2015_cascnn.pdf
Deep Face Recognition Using Deep Convolutional Neural Network http://aiehive.com/deep-face-recognition-using-deep-convolution-neural-network/
Multi-view Face Detection Using Deep Convolutional Neural Networks acm=1492157015_8ffa84e6632810ea05ff005794fed8d5”>http://delivery.acm.org/10.1145/2750000/2749408/p643-farfade.pdf?ip=123.138.79.12&id=2749408&acc=ACTIVE SERVICE&key=BF85BBA5741FDC6E.B37B3B2DF215A17D.4D4702B0C3E38B35.4D4702B0C3E38B35&CFID=923677366&CFTOKEN=37844144&acm=1492157015_8ffa84e6632810ea05ff005794fed8d5
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition https://arxiv.org/pdf/1603.01249.pdf
Wider face: A face detectionbenchmark http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/paper.pdf
Joint training of cascaded cnn for face detection http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Qin_Joint_Training_of_CVPR_2016_paper.pdf
Face detection with end-to-end integration of a convnet and a 3d model https://arxiv.org/pdf/1606.00850.pdf
Face Detection using Deep Learning: An Improved Faster RCNN Approach https://arxiv.org/pdf/1701.08289.pdf
新旧方法对比：
Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks：
Deep Convolutional Network Cascade for Facial Point Detection：
Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification：
DeepFace: Closing the Gap to Human-Level Performance in Face Verification：
Deep learning face representation by joint identification-verification：
Deep learning face representation from predicting 10,000 classes：
Deeply learned face representations are sparse, selective, and robust：
Deepid3: Face recognition with very deep neural networks：
FaceNet: A Unified Embedding for Face Recognition and Clustering：
Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness：
Large-pose Face Alignment via CNN-based Dense 3D Model Fitting：
Unconstrained 3D face reconstruction：
Adaptive contour fitting for pose-invariant 3D face shape reconstruction：
High-fidelity pose and expression normalization for face recognition in the wild：
Adaptive 3D face reconstruction from unconstrained photo collections：
Regressing a 3D face shape from a single image：
Dense 3D face alignment from 2d videos in real-time：
Robust facial landmark detection under significant head poses and occlusion：
A convolutional neural network cascade for face detection：
Deep Face Recognition Using Deep Convolutional Neural Network：
Multi-view Face Detection Using Deep Convolutional Neural Networks：
HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition：
Wider face: A face detectionbenchmark
Joint training of cascaded cnn for face detection：：
Face detection with end-to-end integration of a convnet and a 3d model：
Face Detection using Deep Learning: An Improved Faster RCNN Approach：
3D重建
经典模型：
3D ShapeNets: A Deep Representation for Volumetric Shapes https://people.csail.mit.edu/khosla/papers/cvpr2015_wu.pdf
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction https://arxiv.org/pdf/1604.00449.pdf
Learning to generate chairs with convolutional neural networks https://arxiv.org/pdf/1411.5928.pdf
Category-specific object reconstruction from a single image http://people.eecs.berkeley.edu/~akar/categoryshapes.pdf
Enriching Object Detection with 2D-3D Registration and Continuous Viewpoint Estimation http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7298866
ShapeNet: An Information-Rich 3D Model Repository https://arxiv.org/pdf/1512.03012.pdf
3D reconstruction of synapses with deep learning based on EM Images http://ieeexplore.ieee.org/stamp/stamp.jsp？arnumber=7558866
Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces https://arxiv.org/pdf/1605.06240.pdf
Unsupervised Learning of 3D Structure from Images https://arxiv.org/pdf/1607.00662.pdf
Deep learning 3d shape surfaces using geometry images http://download.springer.com/static/pdf/605/chp%3A10.1007%2F978-3-319-46466-4_14.pdf?originUrl=http://link.springer.com/chapter/10.1007/978-3-319-46466-4_14&token2=exp=1492181498~acl=/static/pdf/605/chp%253A10.1007%252F978-3-319-46466-4_14.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-319-46466-4_14*~hmac=b772943d8cd5f914e7bc84a30ddfdf0ef87991bee1d52717cb4930e3eccb0e63
FPNN: Field Probing Neural Networks for 3D Data https://arxiv.org/pdf/1605.06240.pdf
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views https://arxiv.org/pdf/1505.05641.pdf
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling https://arxiv.org/pdf/1610.07584.pdf
SurfNet: Generating 3D shape surfaces using deep residual networks https://arxiv.org/pdf/1703.04079.pdf
3D ShapeNets: A Deep Representation for Volumetric Shapes：
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction：
Learning to generate chairs with convolutional neural networks：
Category-specific object reconstruction from a single image：
Enriching Object Detection with 2D-3D Registration and Continuous Viewpoint Estimation：
Completing 3d object shape from one depth image：
ShapeNet: An Information-Rich 3D Model Repository：
3D reconstruction of synapses with deep learning based on EM Images：
Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces：
FPNN: Field Probing Neural Networks for 3D Data：
Unsupervised Learning of 3D Structure from Images：
Deep learning 3d shape surfaces using geometry images：
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views：
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling：
SurfNet: Generating 3D shape surfaces using deep residual networks：
推荐系统
经典模型：
Autorec: Autoencoders meet collaborative filtering http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf
User modeling with neural network for review rating prediction https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwj35dyVo6nTAhWEnpQKHSAwCw4QFggjMAA&url=http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/11051/10849&usg=AFQjCNHeMJX8AZzoRF0ODcZE_mXazEktUQ
Collaborative Deep Learning for Recommender Systems https://arxiv.org/pdf/1409.2944.pdf
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf
A neural probabilistic model for context based citation recommendation http://www.personal.psu.edu/wzh112/publications/aaai_slides.pdf
Hybrid Recommender System based on Autoencoders acm=1492356698_958d1b64105cd41b9719c8d285736396”>http://delivery.acm.org/10.1145/2990000/2988456/p11-strub.pdf?ip=123.138.79.12&id=2988456&acc=ACTIVE SERVICE&key=BF85BBA5741FDC6E.B37B3B2DF215A17D.4D4702B0C3E38B35.4D4702B0C3E38B35&CFID=751612499&CFTOKEN=37099060&acm=1492356698_958d1b64105cd41b9719c8d285736396
Wide & Deep Learning for Recommender Systems https://arxiv.org/pdf/1606.07792.pdf
Deep Neural Networks for YouTube Recommendations https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks http://www.wanghao.in/paper/NIPS16_CRAE.pdf
Neural Collaborative Filtering http://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf
Recurrent Recommender Networks http://alexbeutel.com/papers/rrn_wsdm2017.pdf
Autorec: Autoencoders meet collaborative filtering：
User modeling with neural network for review rating prediction：
A neural probabilistic model for context based citation recommendation：
A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems：
Collaborative Deep Learning for Recommender Systems：
Wide & Deep Learning for Recommender Systems：
Deep Neural Networks for YouTube Recommendations：
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks：
Neural Collaborative Filtering：
Recurrent Recommender Networks：
细粒度图像分析
经典模型：
Part-based R-CNNs for Fine-grained Category Detection https://people.eecs.berkeley.edu/~nzhang/papers/eccv14_part.pdf
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets http://www.bmva.org/bmvc/2014/files/paper071.pdf
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition https://arxiv.org/pdf/1605.06878.pdf
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xiao_The_Application_of_2015_CVPR_paper.pdf
Bilinear CNN Models for Fine-grained Visual Recognition http://vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval https://arxiv.org/pdf/1604.04994.pdf
Near Duplicate Image Detection: min-Hash and tf-idf Weighting https://www.robots.ox.ac.uk/~vgg/publications/papers/chum08a.pdf
Fine-grained image search https://users.eecs.northwestern.edu/~jwa368/pdfs/deep_ranking.pdf
Efficient large-scale structured learning http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Branson_Efficient_Large-Scale_Structured_2013_CVPR_paper.pdf
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks https://arxiv.org/pdf/1504.08289.pdf
Part-based R-CNNs for Fine-grained Category Detection：
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification：
Bilinear CNN Models for Fine-grained Visual Recognition：
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval：
Near Duplicate Image Detection: min-Hash and tf-idf Weighting：
Fine-grained image search：
Efficient large-scale structured learning：
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks：
图像压缩
经典模型：
Auto-Encoding Variational Bayes https://arxiv.org/pdf/1312.6114.pdf
k-Sparse Autoencoders https://arxiv.org/pdf/1312.5663.pdf
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction http://www.iro.umontreal.ca/~lisa/pointeurs/ICML2011_explicit_invariance.pdf
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf
Tutorial on Variational Autoencoders https://arxiv.org/pdf/1606.05908.pdf
End-to-end Optimized Image Compression https://openreview.net/pdf?id=rJxdQ3jeg
Guetzli: Perceptually Guided JPEG Encoder https://arxiv.org/pdf/1703.04421.pdf
Auto-Encoding Variational Bayes：
k-Sparse Autoencoders：
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction：
Stacked Denoising Autoencoders: Learning Useful Representa-tions in a Deep Network with a Local Denoising Criterion：
Tutorial on Variational Autoencoders：
End-to-end Optimized Image Compression：
Guetzli: Perceptually Guided JPEG Encoder：
引用块内容 NLP领域 教程：http://cs224d.stanford.edu/syllabus.html 注： 1）目前接触了该领域的一点皮毛，后续会慢慢更新。 2）也希望研究该领域的朋友们做出一些贡献，期待你们的加入。
语音识别领域 注： 1）目前还没有详细了解语音识别领域，后续会慢添加更新。 2）也希望研究该领域的朋友们做出一些贡献，期待你们的加入。
AGI – 通用人工智能领域 注： 1）目前还没有详细了解语音识别领域，后续会慢添加。 2）也希望研究该领域的朋友们做出一些贡献，期待你们的加入。
深度学习引起的一些新的技术：
迁移学习：近些年来在人工智能领域提出的处理不同场景下识别问题的主流方法。相比于浅时代的简单方法，深度神经网络模型具备更加优秀的迁移学习能力。并有一套简单有效的迁移方法，概括来说就是在复杂任务上进行基础模型的预训练（pre-train），在特定任务上对模型进行精细化调整（fine-tune）联合学习（JL）：强化学习（RL）：强化学习(reinforcement learning，又称再励学习，评价学习)是一种重要的机器学习方法，在智能控制机器人及分析预测等领域有许多应用。但在传统的机器学习分类中没有提到过强化学习，而在连接主义学习中，把学习算法分为三种类型，即非监督学习(unsupervised learning)、监督学习(supervised leaning)和强化学习。 视频教程： https://cn.udacity.com/course/reinforcement-learning–ud600注：由于还没有学习到该部分，仅仅知道这个新的概念，后面会慢慢添加进来。
深度强化学习（DRL）： Tutorial：http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf 课程： http://rll.berkeley.edu/deeprlcourse/ DeepMind： https://deepmind.com/blog/deep-reinforcement-learning/终结语
注： 1. 好了，终于差不多啦，为了写这个东西，花费了很多时间，但是通过这个总结以后，我也学到了很多，我真正的认识到DeepLearning已经贯穿了整个CV领域。如果你从事CV领域的话，我建议你花一些时间去了解深度学习吧！毕竟，它正在颠覆这个邻域！ 2. 由于经验有限，可能会有一些错误，希望大家多多包涵。如果你有任何问题，可以你消息给我，我会及时的回复大家。 3. 由于本博客是我自己原创，如需转载，请联系我。 邮箱：1575262785@qq.com
