博客目录

0001-01

Domain Adapation 相关

CNN backbones 结构介绍 LeNet5(1998) AlexNet8(2012) VGG(2014) VGG16 VGG19 GoogLeNet22(2014) Inception v1-v4 ResNet(2015) ResNet50 ResNet101 DRN-26 Semantic Segmentation $p_{ij}$: 被预测为 $j$ 类样本的 $i$ 类样本 Pixel Accuarcy: $\text{PA}=\frac{\sum_{i=0}^kp_{ii}}{\sum_{i=0}^k\sum_{j=0}^kp_{ij}}$ Mean Pixel Accuracy: $\text{MPA}=\frac{1}{k}\sum_{i=0}^k\frac{p_{ii}}{\sum_{j=0}^kp_{ij}}$ Mean Intersection over Union: $\text{MIoU}=\frac{1}{k}\sum_{i=0}^k\frac{p_{ii}}{\sum_{j=0}^kp_{ij}+\sum_{j=0}^kp_{ji}-p_{ii}}$ Frequency Weighted Intersection over Union: $\text{FWIoU}=\frac{1}{\sum_{i=0}^k\sum_{j=0}^kp_{ij}}\sum_{i=0}\frac{p_{ii}\sum_{j=0}^kp_{ij}}{\sum_{j=0}^kp_{ij}+\sum_{j=0}^kp_{ji}-p_{ii}}$ 架构 FCN-8s DeepLab V2 Adversial GAN: $\min_G\max_D V(D,G)=E_{x\sim p_{\text{data}(x)}}[\log D(x)]+E_{z\sim p_z(z)}[\log (1-D(x))]$ cGAN: $\min_G\max_D V(D,G)=E_{x\sim p_{\text{data}(x|y)}}[\log D(x)]+E_{z\sim

0001-01-01 08:05 CST 阅读更多