目录
综述
Deep Learning
论文题目：Deep Learning
论文地址：https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
DeepLearning 3大佬Yann LeCun, Yoshua Bengio & Geoffrey Hinton在Nature上发表的关于DeepLearning的综述文章。
图像分类
LeNet
论文题目：Gradient-Based Learning Applied to Document Recognition
论文地址：http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
第一个卷积神经网络
开源代码：
class Lenet(nn.Module):
def __init__(self, bool_bn=False, num_classes=10):
super(Lenet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 6, kernel_size=5),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.features_bn = nn.Sequential(
nn.Conv2d(3, 6, kernel_size=5),
nn.BatchNorm2d(6),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5),
nn.BatchNorm2d(16),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(120, 84),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(84, num_classes),
)
self.bool_bn = bool_bn
def forward(self, x):
if self.bool_bn:
x = self.features_bn(x)
else:
x = self.features(x)
x = x.view(x.size(0), 16 * 5 * 5)
x = self.classifier(x)
return xAlexNet
论文题目：Imagenet classification with deep convolutional neural networks
2012年ILSVRC分类比赛冠军
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py
ZFNet
论文题目：Visualizing and Understanding Convolutional Networks
论文地址：https://arxiv.org/pdf/1311.2901.pdf
2013年ILSVRC分类比赛冠军
Inception系列
论文题目：Going deeper with convolutions
论文地址：https://arxiv.org/pdf/1409.4842v1.pdf
2014年ILSVRC分类比赛冠军
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/googlenet.py
https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py
论文题目：Rethinking the Inception Architecture for Computer Vision
论文地址：https://arxiv.org/pdf/1512.00567.pdf
Inception v2-v3
论文题目：Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
论文地址：https://arxiv.org/pdf/1602.07261.pdf
Inception-v4, Inception-ResNet-v1, Inception-ResNet-v2
VGG
论文题目：Very Deep Convolutional Networks for Large-Scale Image Recognition
论文地址：https://arxiv.org/pdf/1409.1556.pdf
2014年ILSVRC分类比赛亚军
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
ResNet
论文题目：Deep Residual Learning for Image Recognition
论文地址：https://arxiv.org/pdf/1512.03385.pdf
2015年1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation
论文题目：Identity Mappings in Deep Residual Networks
论文地址：https://arxiv.org/pdf/1603.05027.pdf
修改了ResNet中卷积、批量归一化和Relu层的顺序提高分类效果
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
DenseNet
论文题目：Densely Connected Convolutional Networks
论文地址：https://arxiv.org/pdf/1608.06993.pdf
CVPR2017最佳论文
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
ResNeXt
论文题目：Aggregated Residual Transformations for Deep Neural Networks
论文地址：https://arxiv.org/pdf/1611.05431.pdf
ResNet分组卷积，提高检测准确率；Faster-RCNN Backbone首选网络
开源代码：
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
SENet
论文题目：Squeeze-and-Excitation Networks
论文地址：https://arxiv.org/pdf/1709.01507.pdf
在网络中增加了Squeeze和Excitation分支，提高检测准确率
EfficientNet
论文题目：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
论文地址：https://arxiv.org/pdf/1905.11946.pdf
目标检测
RCNN
论文题目：Rich feature hierarchies for accurate object detection and semantic segmentation
论文地址：https://arxiv.org/pdf/1311.2524.pdf
通过Selective Search选择存在目标的区域，通过SVM实现分类，通过Bounding Box Regression实现定位
OverFeat
论文题目：OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
论文地址：https://arxiv.org/pdf/1312.6229.pdf
通过全卷积网络实现目标定位，winner of the ILSVRC13 localization competition
SPP
论文题目：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
论文地址：https://arxiv.org/pdf/1406.4729.pdf
提出了空间金字塔池化的方法，进行图像分类和目标检测
Fast RCNN
论文题目：Fast R-CNN
论文地址：https://arxiv.org/pdf/1504.08083.pdf
提出了ROI Pooling的方法
Faster RCNN
论文题目：Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
论文地址：https://arxiv.org/pdf/1506.01497.pdf
使用了Region Proposal Net替代Selective Search的方法，实现端到端检测并提高检测速度。
R-FCN
论文题目：R-FCN: Object Detection via Region-based Fully Convolutional Networks
论文地址：https://arxiv.org/pdf/1605.06409.pdf
在ROI中使用k*k的feature map投票预测种类
FPN
论文题目：Feature Pyramid Networks for Object Detection
论文地址：https://arxiv.org/pdf/1612.03144.pdf
提出了图像特征金字塔的方法，提高对图像中小目标的检测精度
SSD系列
论文题目：SSD: Single Shot MultiBox Detector
论文地址：https://arxiv.org/pdf/1512.02325.pdf
一种One Stage Detector
论文题目：DSSD : Deconvolutional Single Shot Detector
论文地址：https://arxiv.org/pdf/1701.06659.pdf
改进了SSD，提高检测精度
YOLO系列
论文题目：You Only Look Once: Unified, Real-Time Object Detection
论文地址：https://arxiv.org/pdf/1506.02640.pdf
代码地址：https://github.com/pjreddie/darknet
https://github.com/AlexeyAB/darknet
一种One Stage Detector，YOLO v1
论文题目：YOLO9000: Better, Faster, Stronger
论文地址：https://arxiv.org/pdf/1612.08242.pdf
YOLO v2， Backbone为Darknet19
论文题目：YOLOv3: An Incremental Improvement
论文地址：https://pjreddie.com/media/files/papers/YOLOv3.pdf
YOLOv3，Backbone为Darknet53，与v2相比，采用了残差模块和FPN结构
论文题目：Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
论文地址：https://arxiv.org/pdf/1904.04620v2.pdf
代码地址：https://github.com/jwchoi384/Gaussian_YOLOv3
RetinaNet
论文题目：Focal Loss for Dense Object Detection
论文地址：https://arxiv.org/pdf/1708.02002.pdf
对于训练集中正负样本不平均的情况，采用了focal loss的方法，减小负样本数量过多的影响。
CornerNet
论文题目：CornerNet: Detecting Objects as Paired Keypoints
论文地址：https://arxiv.org/pdf/1808.01244.pdf
基于关键点的方法进行目标检测，使用了角池化的方法检测不在检测框的左上角或者右下角的目标。
论文题目：CornerNet-Lite: Efficient Keypoint Based Object Detection
论文地址：https://arxiv.org/pdf/1904.08900.pdf
使用了CornerNet Saccade和CornerNet Squeez对CornerNet检测进行提速。
CenterNet
论文题目：CenterNet: Keypoint Triplets for Object Detection
论文地址：https://arxiv.org/pdf/1904.08189.pdf
对CornerNet进行了中心池化和级联角池化的优化，提高了mAP。
论文题目：Objects as Points
论文地址：https://arxiv.org/pdf/1904.07850.pdf
采用了不同的Backbone进行检测比较检测速度和mAP。
CVPR2018
论文题目：Cascade R-CNN Delving into High Quality Object Detection
论文地址：https://arxiv.org/pdf/1712.00726.pdf
论文题目：Relation Networks for Object Detection
论文地址：https://arxiv.org/pdf/1711.11575.pdf
论文题目：Single-Shot Refinement Neural Network for Object Detection
论文地址：https://arxiv.org/pdf/1711.06897.pdf
论文题目：An Analysis of Scale Invariance in Object Detection – SNIP
论文地址：https://arxiv.org/pdf/1711.08189.pdf
论文题目：R-FCN-3000 at 30fps: Decoupling Detection and Classification
论文地址：https://arxiv.org/pdf/1712.01802.pdf
论文题目：Single-Shot Object Detection with Enriched Semantics
论文链接：https://arxiv.org/pdf/1712.00433.pdf
论文题目：Scale-Transferrable Object Detection
论文地址：http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1376.pdf
图像分割
FCN
论文题目：Fully Convolutional Networks for Semantic Segmentation
论文地址：https://arxiv.org/pdf/1411.4038.pdf
使用全连接网络进行图像语义分割
Mask RCNN
论文题目：Mask R-CNN
论文地址：https://arxiv.org/pdf/1703.06870.pdf
ICCV2017最佳论文，在faster rcnn的基础上增加了mask分支，进行实例分割
论文题目：Learning to Segment Every Thing
论文地址：https://arxiv.org/abs/1711.10370
Mask RCNN的升级版，设计了从bbox到mask的weight transfer function
梯度下降
方法综述
论文题目：An overview of gradient descent optimization algorithms
论文地址：https://arxiv.org/pdf/1609.04747.pdf
介绍各种梯度下降方法，比较性能。
Momentum
论文题目：On the momentum term in gradient descent learning algorithms
带动量的SGD
Nesterov
论文题目：A method for unconstrained convex minimization problem with the rate of convergence o(1/k2)
带Nesterov动量的SGD
Adagrad
论文题目：Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
论文地址：http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf
Adagrad算法
Adadelta
论文题目：ADADELTA: An Adaptive Learning Rate Method
论文地址：https://arxiv.org/pdf/1212.5701.pdf
Adagrad算法
Adam
论文题目：Adam: a Method for Stochastic Optimization
论文地址：https://arxiv.org/pdf/1412.6980.pdf
Adam算法
Nesterov+Adam
论文题目：Incorporating Nesterov Momentum into Adam
论文地址：https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ
带Nesterov动量的Adam算法
Adabound
论文题目：Adaptive Gradient Methods with Dynamic Bound of Learning Rate
论文地址：https://arxiv.org/pdf/1902.09843.pdf
ICLR2019最新梯度下降论文
生成对抗网络
GAN
论文题目：Generative Adversarial Networks
论文地址：https://arxiv.org/pdf/1406.2661.pdf
通过对抗训练，生成与训练集类似的数据
DCGAN
论文题目：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
论文地址：https://arxiv.org/pdf/1511.06434.pdf
通过卷积和反卷积网络生成数据
LSGAN
论文题目：Least Squares Generative Adversarial Networks
论文地址：https://arxiv.org/pdf/1611.04076.pdf
损失函数为二次函数训练网络
Wasserstein GAN
论文题目：Wasserstein GAN
论文地址：https://arxiv.org/pdf/1701.07875.pdf
通过修改损失函数，提高生成效果
Age Conditional GAN
论文题目：FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
论文地址：https://arxiv.org/pdf/1702.01983.pdf
生成每个人不同年龄段的照片
Cycle GAN
论文题目：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
论文地址：https://arxiv.org/pdf/1703.10593.pdf
斑马图像和马的图像、苹果图像和橘子图像相互转换
StarGAN
论文题目：StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
论文地址：https://arxiv.org/pdf/1711.09020.pdf
生成明星不同肤色、发型、表情等特征的GAN
CartoonGAN
论文题目：CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
照片转卡通风格
双目匹配
论文题目：Computing the Stereo Matching Cost with a Convolutional Neural Network
论文地址：https://arxiv.org/pdf/1409.4326.pdf
在9x9的图片patch上判断两点是否匹配
论文题目：Learning to Compare Image Patches via Convolutional Neural Networks
论文地址：https://arxiv.org/pdf/1504.03641.pdf
论文题目：Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
论文地址：https://arxiv.org/pdf/1510.05970v2.pdf
论文题目：Efficient Deep Learning for Stereo Matching
论文地址：https://www.cs.toronto.edu/~urtasun/publications/luo_etal_cvpr16.pdf
论文题目：End-to-End Learning of Geometry and Context for Deep Stereo Regression
论文地址：https://arxiv.org/pdf/1703.04309v1.pdf
论文题目：Learning for Disparity Estimation through Feature Constancy
论文地址：https://arxiv.org/pdf/1712.01039.pdf
论文题目：Pyramid Stereo Matching Network
论文地址：https://arxiv.org/pdf/1803.08669.pdf
归一化方法
四种归一化方法说明——Group Normalization, Yuxin Wu, Kaiming He, https://arxiv.org/pdf/1803.08494.pdf
Batch Normalization
论文题目：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
论文地址：https://arxiv.org/pdf/1502.03167.pdf
Batch Normalization批量归一化方法
Layer Normalization
论文题目：Layer Normalization
论文地址：https://arxiv.org/pdf/1607.06450.pdf
Instance Normalization
论文题目：Instance Normalization: The Missing Ingredient for Fast Stylization
论文地址：https://arxiv.org/pdf/1607.08022.pdf
Group Normalization
论文题目：Group Normalization
论文地址：https://arxiv.org/pdf/1803.08494.pdf
组归一化方法
模型剪枝
论文题目：Pruning Filters for Efficient ConvNets
论文地址：https://arxiv.org/pdf/1608.08710.pdf
基于卷积层权重的L1正则化大小作为标准进行剪枝
论文题目：Learning Efficient Convolutional Networks through Network Slimming
论文地址：https://arxiv.org/pdf/1708.06519.pdf
将Batch Normalization层的gamma系数作为标准进行剪枝。
论文题目：Rethinking the Value of Network Pruning
论文地址：https://arxiv.org/pdf/1810.05270.pdf
比较了各种剪枝算法，及剪枝后进行fine tune和train from scratch准确率的比较。
