hjimce算法类博文目录

不念不忘少年蓝@ 2022-04-23 13:50 167阅读 0赞

# hjimce算法类博文目录 #

个人分类： [图像处理][Link 1][深度学习][Link 2][机器学习][Link 3][图形处理][Link 4][数据挖掘][Link 5][基础知识][Link 6][算法移植优化][Link 7]

原博客地址： https://blog.csdn.net/hjimce/article/details/50573444

**hjimce算法类博文目录**

**[一、深度学习][Link 8]**

[深度学习（七十四）半监督Mean teachers][Mean teachers]

[深度学习(七十三)pytorch学习笔记][pytorch]

[深度学习(七十二)tensorflow 集群训练][tensorflow]

[深度学习（七十一）3D CNN时空特征学习][3D CNN]

[深度学习（七十）darknet 实现实验mobilenet][darknet _mobilenet]

[深度学习（六十九）darknet 实现实验 Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffma][darknet _ Compressing Deep Neural Networks with Pruning_ Trained Quantization and Huffma]

[深度学习（六十八）darknet使用][darknet]

[深度学习（六十七）metal forge深度学习库使用][metal forge]

[深度学习（六十六）生成模型、最大化似然、KL散度][KL]

[深度学习（六十五）移动端网络MobileNets][MobileNets]

[深度学习（六十四）Faster R-CNN物体检测][Faster R-CNN]

[深度学习（六十三）空间变换网络][Link 9]

[深度学习（六十二）SqueezeNet网络设计思想笔记][SqueezeNet]

[深度学习（六十一）NNPACK 移植与实验][NNPACK]

[深度学习（六十）网络压缩简单总结][Link 10]

[深度学习（五十九）mxnet移植至android][mxnet_android]

[深度学习（五十八）caffe移植至mxnet][caffe_mxnet]

[深度学习（五十七）tensorflow andorid yolo物体检测测试][tensorflow andorid yolo]

[深度学习（五十六）tensorflow项目构建流程][tensorflow 1]

[深度学习（五十五）tensorflow分布式训练][tensorflow 2]

[深度学习（五十四）图片翻译WGAN实验测试][WGAN]

[深度学习（五十三）对抗网络][Link 11]

[深度学习（五十二）变分贝叶斯自编码器(下)][Link 12]

[深度学习（五十一）变分贝叶斯自编码器(上)][Link 13]

[深度学习（五十）基于条件对抗网络的图片翻译][Link 14]

[深度学习（四十九）Tensorflow提高代码效率笔记][Tensorflow]

[深度学习（四十八）InfoGAN学习笔记][InfoGAN]

[深度学习（四十七）DSD正则化训练方法][DSD]

[深度学习（四十六）Adversarial Autoencoders][Adversarial Autoencoders]

[深度学习（四十五）条件对抗网络][Link 15]

[深度学习（四十四）变分贝叶斯自编码器（上）][Link 16]

[深度学习（四十三）条件变分自编码器概述][Link 17]

[深度学习（四十二）word2vec词向量学习笔记][word2vec]

[深度学习（四十一）cuda8.0+ubuntu16.04+theano、caffe、tensorflow环境搭建][cuda8.0_ubuntu16.04_theano_caffe_tensorflow]

[深度学习（四十）caffe使用点滴记录][caffe]

[深度学习（三十九）可视化理解卷积神经网络(2.0)][2.0]

[深度学习（三十八）卷积神经网络入门学习(2.0)][2.0 1]

[深度学习（三十七）优化求解系列之(1)简单理解梯度下降][1]

[深度学习（三十六）异构计算CUDA学习笔记（1）][CUDA_1]

[深度学习（三十五）异构计算GLSL学习笔记（1）][GLSL_1]

[深度学习（三十四）对抗自编码网络-未完待续][-]

[深度学习（三十三）CRF as RNN语义分割-未完待续][CRF as RNN_-]

[深度学习（三十二）半监督阶梯网络学习笔记][Link 18]

[深度学习（三十一）基于深度矩阵分解的属性表征学习][Link 19]

[深度学习（三十）贪婪深度字典学习][Link 20]

[深度学习（二十九）Batch Normalization 学习笔记][Batch Normalization]

[深度学习（二十八）基于多尺度深度网络的单幅图像深度估计][Link 21]

[深度学习（二十七）可视化理解卷积神经网络][Link 22]

[深度学习（二十六）Network In Network学习笔记][Network In Network]

[深度学习（二十五）基于Mutil-Scale CNN的图片语义分割、法向量估计][Mutil-Scale CNN]

[深度学习（二十四）矩阵分解之基于k-means的特征表达学习][k-means]

[深度学习（二十三）Maxout网络学习][Maxout]

[深度学习（二十二）Dropout浅层理解与实现][Dropout]

[深度学习（二十一）基于FCN的图像语义分割][FCN]

[深度学习（二十）基于Overfeat的图片分类、定位、检测][Overfeat]

[深度学习（十九）基于空间金字塔池化的卷积神经网络物体检测][Link 23]

[深度学习（十八）基于R-CNN的物体检测][R-CNN]

[深度学习（十七）基于改进Coarse-to-fine CNN网络的人脸特征点定位][Coarse-to-fine CNN]

[深度学习（十六）基于2-channel network的图片相似度判别][2-channel network]

# [深度学习（十五）基于级联卷积神经网络的人脸特征点定位][Link 24] #

[深度学习（十四）基于CNN的性别、年龄识别][CNN]

[深度学习（十三）caffe之训练数据格式][caffe 1]

[深度学习（十二）从自编码到栈式自编码][Link 25]

[深度学习（十一）RNN入门学习][RNN]

[深度学习（十）keras学习笔记][keras]

[深度学习（九）caffe预测、特征可视化python接口调用][caffe_python]

[深度学习（八）RBM受限波尔兹曼机学习-未完待续][RBM_-]

[深度学习（七）caffe源码c++学习笔记][caffe_c]

[深度学习（六）caffe入门学习][caffe 2]

[深度学习（五）caffe环境搭建][caffe 3]

[深度学习（四）卷积神经网络入门学习][Link 26]

[深度学习（三）theano入门学习][theano]

[深度学习（二）theano环境搭建][theano 1]

[深度学习（一）深度学习学习资料][Link 27]

[从零开始编写深度学习库（五）PoolingLayer 网络层CPU编写][PoolingLayer _CPU]

[从零开始编写深度学习库（五）Eigen Tensor学习笔记2.0][Eigen Tensor_2.0]

[从零开始编写深度学习库（四）Eigen::Tensor学习使用及代码重构][Eigen_Tensor]

[从零开始编写深度学习库（三）ActivationLayer网络层CPU实现][ActivationLayer_CPU]

[从零开始编写深度学习库（二）FullyconnecteLayer CPU编写][FullyconnecteLayer CPU]

[从零开始编写深度学习库（一）SoftmaxWithLoss CPU编写][SoftmaxWithLoss CPU]

**[三、机器学习基础实践][Link 28]**

[机器学习（十四）Libsvm学习笔记][Libsvm]

[机器学习（十三）k-svd字典学习][k-svd]

[机器学习（十二）朴素贝叶斯分类][Link 29]

[机器学习（十一）谱聚类算法][Link 30]

[机器学习（十）Mean Shift 聚类算法][Mean Shift]

[机器学习（九）初识BP神经网络][BP]

[机器学习（八）Apriori算法学习][Apriori]

[机器学习（七）白化whitening][whitening]

[机器学习（六）非负矩阵分解NMF-未完待续][NMF-]

[机器学习（五）PCA数据降维][PCA]

[机器学习（四）高斯混合模型][Link 31]

[机器学习（三）k均值聚类][k]

[机器学习（二）逻辑回归][Link 32]

[机器学习（一）线性回归][Link 33]

**[四、图像处理][Link 34]**

[图像处理（二十三）基于调色板的图像Recoloring][Recoloring]

[图像处理（二十二）贝叶斯抠图][Link 35]

[图像处理（二十一）基于数据驱动的人脸卡通动画生成][Link 36]

[图像处理（十九）基于移动最小二乘的图像变形][Link 37]

[图像处理（十四）图像分割(4)grab cut的图割实现][4_grab cut]

[图像处理（十三）保刚性图像变形算法][Link 38]

[图像处理（十二）图像融合(1)Seamless cloning泊松克隆][1_Seamless cloning]

[图像处理（十一）图像分割(3)泛函能量LevelSet、snake分割][3_LevelSet_snake]

[图像处理（九）人物肖像风格转换][Link 39]

[图像处理（十）基于特征线的图像变形-][- 1]

[图像处理（七）导向滤波磨皮][Link 40]

[图像处理（六）递归双边滤波磨皮][Link 41]

[图像处理（四）图像分割(2)测地距离Geodesic图割][2_Geodesic]

[图像处理（三）图像分割(1)Random Walks分割][1_Random Walks]

[图像处理（二）Seam Carving算法][Seam Carving]

[图像处理（一）图像变形(1)矩形全景图像还原][1 1]

**[五、三维图形][Link 42]**

[图形处理（十二）拉普拉斯网格优化、最小二乘网格模型光顺][Link 43]

[图形处理（十一）Stroke Parameterization][Stroke Parameterization]

[图形处理（十）测地极坐标参数化][Link 44]

[图形处理（九）点云重建（下）法矢求取、有向距离场等值面提取][Link 45]

[图形处理（八）点云重建（上）点云滤波、尖锐特征边增采样、移除离群点][Link 46]

[图形处理（七）基于热传播的测地距离计算][Link 47]

[图形处理（六）拖拽式网格融合][Link 48]

[图形处理（五）基于旋转不变量的网格变形][Link 49]

[图形处理（四）基于梯度场的网格编辑][Link 50]

[图形处理（三）简单拉普拉斯网格变形][Link 51]

[图形处理（二）固定边界参数化][Link 52]

[图形处理（一）离散指数映射参数化][Link 53]

**[六、基础知识][Link 54]**

[基础知识（一）matlab与c++混合编程之环境搭建][matlab_c]

[基础知识（二）matlab与c++混合编程之经验笔记][matlab_c 1]

[基础知识（四）Dijkstra算法][Dijkstra]

[基础知识（五）对齐变换相关函数][Link 55]

# [基础知识（六）KD-Tree快速最近邻搜索][KD-Tree] #

# [基础知识（十一）Eigen求解稀疏矩阵][Eigen] #

[基础知识（十二）Opengl回顾记录][Opengl]

[基础知识(十三)dlib python人脸检测 特征点定位][dlib python_]

[基础知识（十七）android gpuimage使用][android gpuimage]

……（略）

** [七、算法移植与优化][Link 56]**

[算法移植优化（七）cmake常用函数记录][cmake]

[算法移植优化（四）c++11 多线程][c_11]

[算法移植优化（三）android dlib 人脸检测使用][android dlib]

[算法移植优化（二）android gpuimage使用][android gpuimage]

[Link 1]: https://blog.csdn.net/hjimce/article/category/3085661
[Link 2]: https://blog.csdn.net/hjimce/article/category/3163421
[Link 3]: https://blog.csdn.net/hjimce/article/category/3145103
[Link 4]: https://blog.csdn.net/hjimce/article/category/5570255
[Link 5]: https://blog.csdn.net/hjimce/article/category/3164775
[Link 6]: https://blog.csdn.net/hjimce/article/category/3208027
[Link 7]: https://blog.csdn.net/hjimce/article/category/6816799
[Link 8]: http://blog.csdn.net/hjimce/article/category/3163421
[Mean teachers]: https://blog.csdn.net/hjimce/article/details/80551721
[pytorch]: https://blog.csdn.net/hjimce/article/details/79686464
[tensorflow]: https://blog.csdn.net/hjimce/article/details/79650210
[3D CNN]: http://blog.csdn.net/hjimce/article/details/79623730
[darknet _mobilenet]: http://blog.csdn.net/hjimce/article/details/76175802
[darknet _ Compressing Deep Neural Networks with Pruning_ Trained Quantization and Huffma]: http://blog.csdn.net/hjimce/article/details/74316731
[darknet]: http://blog.csdn.net/hjimce/article/details/74454165
[metal forge]: http://blog.csdn.net/hjimce/article/details/73469679
[KL]: http://blog.csdn.net/hjimce/article/details/73770644
[MobileNets]: http://blog.csdn.net/hjimce/article/details/72831171
[Faster R-CNN]: http://blog.csdn.net/hjimce/article/details/73382553
[Link 9]: http://blog.csdn.net/hjimce/article/details/72904764
[SqueezeNet]: http://blog.csdn.net/hjimce/article/details/72809131
[NNPACK]: http://blog.csdn.net/hjimce/article/details/65938852
[Link 10]: http://blog.csdn.net/hjimce/article/details/51564774
[mxnet_android]: http://blog.csdn.net/hjimce/article/details/70285787
[caffe_mxnet]: http://blog.csdn.net/hjimce/article/details/70229090
[tensorflow andorid yolo]: http://blog.csdn.net/hjimce/article/details/72383814
[tensorflow 1]: http://blog.csdn.net/hjimce/article/details/51899683
[tensorflow 2]: http://blog.csdn.net/hjimce/article/details/61197190
[WGAN]: http://blog.csdn.net/hjimce/article/details/60346089
[Link 11]: http://blog.csdn.net/hjimce/article/details/57082684
[Link 12]: http://blog.csdn.net/hjimce/article/details/57082788
[Link 13]: http://blog.csdn.net/hjimce/article/details/56678413
[Link 14]: http://blog.csdn.net/hjimce/article/details/56293776
[Tensorflow]: http://blog.csdn.net/hjimce/article/details/56292982
[InfoGAN]: http://blog.csdn.net/hjimce/article/details/55657325
[DSD]: http://blog.csdn.net/hjimce/article/details/54564429
[Adversarial Autoencoders]: http://blog.csdn.net/hjimce/article/details/54411244
[Link 15]: http://blog.csdn.net/hjimce/article/details/54407981
[Link 16]: http://blog.csdn.net/hjimce/article/details/54233192
[Link 17]: http://blog.csdn.net/hjimce/article/details/54096472
[word2vec]: http://blog.csdn.net/hjimce/article/details/51564783
[cuda8.0_ubuntu16.04_theano_caffe_tensorflow]: http://blog.csdn.net/hjimce/article/details/51999566
[caffe]: http://blog.csdn.net/hjimce/article/details/51884024
[2.0]: http://blog.csdn.net/hjimce/article/details/51762046
[2.0 1]: http://blog.csdn.net/hjimce/article/details/51761865
[1]: http://blog.csdn.net/hjimce/article/details/51590307
[CUDA_1]: http://blog.csdn.net/hjimce/article/details/51506207
[GLSL_1]: http://blog.csdn.net/hjimce/article/details/51475644
[-]: http://blog.csdn.net/hjimce/article/details/50968814
[CRF as RNN_-]: http://blog.csdn.net/hjimce/article/details/50888915
[Link 18]: http://blog.csdn.net/hjimce/article/details/50877704
[Link 19]: http://blog.csdn.net/hjimce/article/details/50876956
[Link 20]: http://blog.csdn.net/hjimce/article/details/50876891
[Batch Normalization]: http://blog.csdn.net/hjimce/article/details/50866313
[Link 21]: http://blog.csdn.net/hjimce/article/details/50569474
[Link 22]: http://blog.csdn.net/hjimce/article/details/50544370
[Network In Network]: http://blog.csdn.net/hjimce/article/details/50458190
[Mutil-Scale CNN]: http://blog.csdn.net/hjimce/article/details/50443995
[k-means]: http://blog.csdn.net/hjimce/article/details/50429317
[Maxout]: http://blog.csdn.net/hjimce/article/details/50414467
[Dropout]: http://blog.csdn.net/hjimce/article/details/50413257
[FCN]: http://blog.csdn.net/hjimce/article/details/50268555
[Overfeat]: http://blog.csdn.net/hjimce/article/details/50187881
[Link 23]: http://blog.csdn.net/hjimce/article/details/50187655
[R-CNN]: http://blog.csdn.net/hjimce/article/details/50187029
[Coarse-to-fine CNN]: http://blog.csdn.net/hjimce/article/details/50099115
[2-channel network]: http://blog.csdn.net/hjimce/article/details/50098483
[Link 24]: http://blog.csdn.net/hjimce/article/details/49955149
[CNN]: http://blog.csdn.net/hjimce/article/details/49255013
[caffe 1]: http://blog.csdn.net/hjimce/article/details/49248231
[Link 25]: http://blog.csdn.net/hjimce/article/details/49106869
[RNN]: http://blog.csdn.net/hjimce/article/details/49095371
[keras]: http://blog.csdn.net/hjimce/article/details/49095199
[caffe_python]: http://blog.csdn.net/hjimce/article/details/48972877
[RBM_-]: http://blog.csdn.net/hjimce/article/details/48949197
[caffe_c]: http://blog.csdn.net/hjimce/article/details/48933845
[caffe 2]: http://blog.csdn.net/hjimce/article/details/48933813
[caffe 3]: http://blog.csdn.net/hjimce/article/details/48781693
[Link 26]: http://blog.csdn.net/hjimce/article/details/47323463
[theano]: http://blog.csdn.net/hjimce/article/details/46806923
[theano 1]: http://blog.csdn.net/hjimce/article/details/46654229
[Link 27]: http://blog.csdn.net/hjimce/article/details/45421595
[PoolingLayer _CPU]: http://blog.csdn.net/hjimce/article/details/75045763
[Eigen Tensor_2.0]: http://blog.csdn.net/hjimce/article/details/72764273
[Eigen_Tensor]: http://blog.csdn.net/hjimce/article/details/71710893
[ActivationLayer_CPU]: http://blog.csdn.net/hjimce/article/details/71643717
[FullyconnecteLayer CPU]: http://blog.csdn.net/hjimce/article/details/71630094
[SoftmaxWithLoss CPU]: http://blog.csdn.net/hjimce/article/details/71603118
[Link 28]: http://blog.csdn.net/hjimce/article/category/3145103
[Libsvm]: http://blog.csdn.net/hjimce/article/details/46971039
[k-svd]: http://blog.csdn.net/hjimce/article/details/50810129
[Link 29]: http://blog.csdn.net/hjimce/article/details/46054739
[Link 30]: http://blog.csdn.net/hjimce/article/details/45749757
[Mean Shift]: http://blog.csdn.net/hjimce/article/details/45718593
[BP]: http://blog.csdn.net/hjimce/article/details/45457181
[Apriori]: http://blog.csdn.net/hjimce/article/details/45438507
[whitening]: http://blog.csdn.net/hjimce/article/details/50864602
[NMF-]: http://blog.csdn.net/hjimce/article/details/50849659
[PCA]: http://blog.csdn.net/hjimce/article/details/45000221
[Link 31]: http://blog.csdn.net/hjimce/article/details/45244603
[k]: http://blog.csdn.net/hjimce/article/details/45200985
[Link 32]: http://blog.csdn.net/hjimce/article/details/45418933
[Link 33]: http://blog.csdn.net/hjimce/article/details/45418645
[Link 34]: http://blog.csdn.net/hjimce/article/category/3085661
[Recoloring]: http://blog.csdn.net/hjimce/article/details/49226067
[Link 35]: http://blog.csdn.net/hjimce/article/details/47667947
[Link 36]: http://blog.csdn.net/hjimce/article/details/47083321
[Link 37]: http://blog.csdn.net/hjimce/article/details/46550001
[4_grab cut]: http://blog.csdn.net/hjimce/article/details/45932569
[Link 38]: http://blog.csdn.net/hjimce/article/details/45766321
[1_Seamless cloning]: http://blog.csdn.net/hjimce/article/details/45716603
[3_LevelSet_snake]: http://blog.csdn.net/hjimce/article/details/45586727
[Link 39]: http://blog.csdn.net/hjimce/article/details/45534333
[- 1]: http://blog.csdn.net/hjimce/article/details/45531039
[Link 40]: http://blog.csdn.net/hjimce/article/details/45421299
[Link 41]: http://blog.csdn.net/hjimce/article/details/45421207
[2_Geodesic]: http://blog.csdn.net/hjimce/article/details/45221607
[1_Random Walks]: http://blog.csdn.net/hjimce/article/details/45201263
[Seam Carving]: http://blog.csdn.net/hjimce/article/details/44916869
[1 1]: http://blog.csdn.net/hjimce/article/details/44904271
[Link 42]: http://blog.csdn.net/hjimce/article/category/5570255
[Link 43]: http://blog.csdn.net/hjimce/article/details/46505863
[Stroke Parameterization]: http://blog.csdn.net/hjimce/article/details/46489913
[Link 44]: http://blog.csdn.net/hjimce/article/details/46489899
[Link 45]: http://blog.csdn.net/hjimce/article/details/46419179
[Link 46]: http://blog.csdn.net/hjimce/article/details/46415909
[Link 47]: http://blog.csdn.net/hjimce/article/details/46415499
[Link 48]: http://blog.csdn.net/hjimce/article/details/46415461
[Link 49]: http://blog.csdn.net/hjimce/article/details/46415435
[Link 50]: http://blog.csdn.net/hjimce/article/details/46415291
[Link 51]: http://blog.csdn.net/hjimce/article/details/46415239
[Link 52]: http://blog.csdn.net/hjimce/article/details/46415051
[Link 53]: http://blog.csdn.net/hjimce/article/details/46415009
[Link 54]: http://blog.csdn.net/hjimce/article/category/3208027
[matlab_c]: http://blog.csdn.net/hjimce/article/details/45244935
[matlab_c 1]: http://blog.csdn.net/hjimce/article/details/45167679
[Dijkstra]: http://blog.csdn.net/hjimce/article/details/45751503
[Link 55]: http://blog.csdn.net/hjimce/article/details/47019223
[KD-Tree]: http://blog.csdn.net/hjimce/article/details/47297241
[Eigen]: http://blog.csdn.net/hjimce/article/details/50669931
[Opengl]: http://blog.csdn.net/hjimce/article/details/50669945
[dlib python_]: http://blog.csdn.net/hjimce/article/details/51307886
[android gpuimage]: http://blog.csdn.net/hjimce/article/details/52749846
[Link 56]: http://blog.csdn.net/hjimce/article/category/6816799
[cmake]: http://blog.csdn.net/hjimce/article/details/68953605
[c_11]: http://blog.csdn.net/hjimce/article/details/65441285
[android dlib]: http://blog.csdn.net/hjimce/article/details/64127654