stem/AI/Neural Networks/CNN/GAN/DC-GAN.md
Andy Pack efa7a84a8b vault backup: 2023-12-27 21:56:22
Affected files:
.obsidian/graph.json
.obsidian/workspace-mobile.json
.obsidian/workspace.json
Languages/Spanish/Spanish.md
STEM/AI/Classification/Classification.md
STEM/AI/Classification/Decision Trees.md
STEM/AI/Classification/Logistic Regression.md
STEM/AI/Classification/Random Forest.md
STEM/AI/Classification/Supervised/SVM.md
STEM/AI/Classification/Supervised/Supervised.md
STEM/AI/Neural Networks/Activation Functions.md
STEM/AI/Neural Networks/CNN/CNN.md
STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md
STEM/AI/Neural Networks/CNN/GAN/GAN.md
STEM/AI/Neural Networks/Deep Learning.md
STEM/AI/Neural Networks/Properties+Capabilities.md
STEM/AI/Neural Networks/SLP/Perceptron Convergence.md
2023-12-27 21:56:22 +00:00

74 lines
2.0 KiB
Markdown

---
tags:
- ai
- media
---
Deep [Convolutional](../../../../Signal%20Proc/Convolution.md) [GAN](GAN.md)
![dc-gan](../../../../img/dc-gan.png)
- Generator
- [FCN](../FCN/FCN.md)
- Decoder
- Generate image from code
- Low-dimensional
- ~100-D
- Reshape to [tensor](../../../../Maths/Tensor.md)
- [UpConv](../UpConv.md) to image
- Train using Gaussian random noise for code
- Discriminator
- Contractive
- Cross-entropy [loss](../../Deep%20Learning.md#Loss%20Function)
- [Conv](../Convolutional%20Layer.md) and leaky [ReLu](../../Activation%20Functions.md#ReLu) layers only
- Normalised output via [sigmoid](../../Activation%20Functions.md#Sigmoid)
## [Loss](../../Deep%20Learning.md#Loss%20Function)
$$D(S,L)=-\sum_iL_ilog(S_i)$$
- $S$
- $(0.1, 0.9)^T$
- 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](../FCN/FCN.md)
- Reproducing texture at high level
- Cues triggered by code vector
- Input random noise
- Iteratively improves visual feasibility
- Different to [FCN](../FCN/FCN.md)
- Discriminator is a task specific classifier #classification
- Difficult to train over diverse footage
- Mixing concepts doesn't work
- Single category/class