andy
7052c8c915
Affected files: .obsidian/graph.json .obsidian/workspace.json STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.md STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md STEM/AI/Neural Networks/CNN/GAN/CycleGAN.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/GAN/cGAN.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/img/am-process.png STEM/img/am.png STEM/img/fcn-arch.png STEM/img/fcn-eval.png STEM/img/fcn-uses.png STEM/img/flownet-encode.png STEM/img/flownet-training.png STEM/img/flownet-upconv.png STEM/img/flownet.png STEM/img/super-res.png STEM/img/superres-results.png
867 B
867 B
Fully Convolutional Network
Convolutional and up-convolutional layers with Activation Functions#ReLu but no others (pooling)
- All some sort of Encoder-Decoder
Contractive → UpConv
Image Sementation
- For visual output
- Previously image
\rightarrow
vector
- Previously image
- Additional layers to up-sample representation to an image
- Up-convolutional
- De-convolutional
Training
- Rarely from scratch
- Pre-trained weights
- Replace final layers
- FC layers
- White-noise initialised
- Add upconv layer(s)
- Fine-tune train
- Freeze others
- Annotated GT images
- Can use summed per-pixel log loss
Evaluation
- SDS
- Classical method
- 52% mAP
- FCN
- 62% mAP
- Intersection over Union
- IOU
- Jaccard
- Averaged over all images
J(A,B)=\frac{|A\cap B|}{|A\cup B|}