andy
8f0b604256
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/Convolutional Layer.md STEM/AI/Neural Networks/CNN/Examples.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/StackGAN.md STEM/AI/Neural Networks/CNN/GAN/cGAN.md STEM/AI/Neural Networks/CNN/Inception Layer.md STEM/AI/Neural Networks/CNN/Max Pooling.md STEM/AI/Neural Networks/CNN/Normalisation.md STEM/AI/Neural Networks/CV/Data Manipulations.md STEM/AI/Neural Networks/CV/Datasets.md STEM/AI/Neural Networks/CV/Filters.md STEM/AI/Neural Networks/CV/Layer Structure.md STEM/AI/Neural Networks/Weight Init.md STEM/img/alexnet.png STEM/img/cgan-example.png STEM/img/cgan.png STEM/img/cnn-cv-layer-arch.png STEM/img/cnn-descriptor.png STEM/img/cnn-normalisation.png STEM/img/code-vector-math-for-control-results.png STEM/img/cvmfc.png STEM/img/cyclegan-results.png STEM/img/cyclegan.png STEM/img/data-aug.png STEM/img/data-whitening.png STEM/img/dc-gan.png STEM/img/fine-tuning-freezing.png STEM/img/gabor.png STEM/img/gan-arch.png STEM/img/gan-arch2.png STEM/img/gan-results.png STEM/img/gan-training-discriminator.png STEM/img/gan-training-generator.png STEM/img/googlenet-auxilliary-loss.png STEM/img/googlenet-inception.png STEM/img/googlenet.png STEM/img/icv-pos-neg-examples.png STEM/img/icv-results.png STEM/img/inception-layer-arch.png STEM/img/inception-layer-effect.png STEM/img/lenet-1989.png STEM/img/lenet-1998.png STEM/img/max-pooling.png STEM/img/stackgan-results.png STEM/img/stackgan.png STEM/img/under-over-fitting.png STEM/img/vgg-arch.png STEM/img/vgg-spec.png STEM/img/word2vec.png
1.6 KiB
1.6 KiB
- Generator
- FCN
- Decoder
- Generate image from code
- Low-dimensional
- ~100-D
- Low-dimensional
- Reshape to tensor
- Upconv to image
- Train using Gaussian random noise for code
- Discriminator
- Contractive
- Cross-entropy loss
- Conv and leaky Activation Functions#ReLu layers only
- Normalised output via sigmoid
Loss
D(S,L)=-\sum_iL_ilog(S_i)
S
(0.1, 0.9)^T
- Score generated by discriminator
L
(1, 0)^T
- One-hot label vector
- Step 1
- Depends on choice of real/fake
- Step 2
- One-hot fake vector
\sum_i
- Sum over all images in mini-batch
Noise | Image |
---|---|
z |
x |
- Generator wants
D(G(z))=1
- Wants to fool discriminator
- Discriminator wants
D(G(z))=0
- Wants to correctly catch generator
- Real data wants
D(x)=1
J^{(D)}=-\frac 1 2 \mathbb E_{x\sim p_{data}}\log D(x)-\frac 1 2 \mathbb E_z\log (1-D(G(z)))
J^{(G)}=-J^{(D)}
- First term for real images
- Second term for fake images
Mode Collapse
- Generator gives easy solution
- Learns one image for most noise that will fool discriminator
- Mitigate by minibatch discriminator
- Match G(z) distribution to x
What is Learnt?
- Encoding texture/patch detail from training set
- Similar to FCN
- Reproducing texture at high level
- Cues triggered by code vector
- Input random noise
- Iteratively improves visual feasibility
- Different to FCN
- Discriminator is a task specific classifier
- Difficult to train over diverse footage
- Mixing concepts doesn't work
- Single category/class