深度学习相关论文阅读
Posted NodYoung
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了深度学习相关论文阅读相关的知识,希望对你有一定的参考价值。
目标检测
OverFeat
文章:《OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks》
rcnn
代码:https://github.com/rbgirshick/rcnn,prototxt文件在\\rcnn-master\\finetuning\\voc_2012_prototxt
fast-rcnn
文章:《Fast R-CNN》
代码:https://github.com/rbgirshick/fast-rcnn,prototxt文件在.\\fast-rcnn-master\\models\\CaffeNet
faster-rcnn
文章:《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks》
代码:https://github.com/rbgirshick/py-faster-rcnn,prototxt文件在.\\py-faster-rcnn-master\\models\\pascal_voc\\VGG16\\faster_rcnn_end2end
tensorflow的代码:tf-faster-rcnn代码理解
YOLO
文章:《You Only Look Once: Unified, Real-Time Object Detection》
代码:https://github.com/xingwangsfu/caffe-yolo
文章:《YOLO9000: Better, Faster, Stronger》
SSD
文章:《SSD: Single Shot MultiBox Detector》
代码:https://github.com/weiliu89/caffe/tree/ssd
由于SSD中需要运行python examples/ssd/ssd_pascal.py
来生成solver.prototxt, train.prototxt, test.prototxt, deploy.prototxt。如果你没有硬件运行代码或嫌麻烦,可以在这里直接看https://gist.github.com/JeffOwOSun/1c284e6177c38b17d83da1db7b8c8ce7
FPN
文章:《Feature Pyramid Networks for Object Detection》
mask rcnn
文章:《Mask R-CNN》
语义分割
FCN
文章:《Fully Convolutional Networks for Semantic Segmentation》
代码:https://github.com/shekkizh/FCN.tensorflow
网络结构
VGG
文章:《Very Deep Convolutional Networks for Large-scale image recognition》
googleNet
文章:
Inception v1:《Going Deeper with Convolutions》
Inception v2:《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate 》
Inception v3:《Rethinking the Inception Architecture for Computer Vision》
resnet
文章:《Deep Residual Learning for Image Recognition》
代码:https://github.com/KaimingHe/deep-residual-networks
补充文章:《Residual Networks Behave Like Ensembles of Relatively Shallow Networks》
技巧
梯度递减优化算法综述
《An overview of gradient descent optimization algorithms*》
dropout防止过拟合
《Dropout: A Simple Way to Prevent Neural Networks from
Overfitting》
其他
《Neural Style Transfer: A Review》
相关解读:神经风格迁移研究概述:从当前研究到未来方向(附论文和代码)
以上是关于深度学习相关论文阅读的主要内容,如果未能解决你的问题,请参考以下文章
论文阅读|图神经网络+Actor-Critic求解静态JSP(End-to-End DRL)《基于深度强化学习的调度规则学习》(附带源码)