Andy Pack
6882b75949
Affected files: .obsidian/graph.json .obsidian/workspace.json 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/CV/Visual Search/Visual Search.md Tattoo/Artists.md Tattoo/Engineering.md Tattoo/Flowers.md Tattoo/Music.md Tattoo/Plans.md Tattoo/Sources.md
75 lines
2.0 KiB
Markdown
75 lines
2.0 KiB
Markdown
---
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tags:
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- ai
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- media
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- art
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---
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Deep [Convolutional](../../../../Signal%20Proc/Convolution.md) [GAN](GAN.md)
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- Generator
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- [FCN](../FCN/FCN.md)
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- Decoder
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- Generate image from code
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- Low-dimensional
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- ~100-D
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- Reshape to [tensor](../../../../Maths/Tensor.md)
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- [UpConv](../UpConv.md) to image
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- Train using Gaussian random noise for code
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- Discriminator
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- Contractive
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- Cross-entropy [loss](../../Deep%20Learning.md#Loss%20Function)
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- [Conv](../Convolutional%20Layer.md) and leaky [ReLu](../../Activation%20Functions.md#ReLu) layers only
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- Normalised output via [sigmoid](../../Activation%20Functions.md#Sigmoid)
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## [Loss](../../Deep%20Learning.md#Loss%20Function)
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$$D(S,L)=-\sum_iL_ilog(S_i)$$
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- $S$
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- $(0.1, 0.9)^T$
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- Score generated by discriminator
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- $L$
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- $(1, 0)^T$
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- One-hot label vector
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- Step 1
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- Depends on choice of real/fake
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- Step 2
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- One-hot fake vector
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- $\sum_i$
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- Sum over all images in mini-batch
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| Noise | Image |
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| ----- | ----- |
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| $z$ | $x$ |
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- Generator wants
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- $D(G(z))=1$
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- Wants to fool discriminator
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- Discriminator wants
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- $D(G(z))=0$
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- Wants to correctly catch generator
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- Real data wants
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- $D(x)=1$
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$$J^{(D)}=-\frac 1 2 \mathbb E_{x\sim p_{data}}\log D(x)-\frac 1 2 \mathbb E_z\log (1-D(G(z)))$$
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$$J^{(G)}=-J^{(D)}$$
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- First term for real images
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- Second term for fake images
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# Mode Collapse
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- Generator gives easy solution
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- Learns one image for most noise that will fool discriminator
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- Mitigate by minibatch discriminator
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- Match G(z) distribution to x
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# What is Learnt?
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- Encoding texture/patch detail from training set
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- Similar to [FCN](../FCN/FCN.md)
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- Reproducing texture at high level
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- Cues triggered by code vector
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- Input random noise
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- Iteratively improves visual feasibility
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- Different to [FCN](../FCN/FCN.md)
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- Discriminator is a task specific classifier #classification
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- Difficult to train over diverse footage
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- Mixing concepts doesn't work
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- Single category/class |