17
Generative adversarial networks
dlfc_notes
Preface
1
The Deep Learning Revolution
2
Probabilities
3
Standard distributions
4
Single layer networks: regression
5
Single layer networks: classification
6
Deep Neural Networks
7
Gradient Descent
8
Backpropagation
9
Regularisation
10
Convolutional networks
11
structured distributions
12
Transformers
13
Graph neural networks
14
Sampling
15
Discrete latent variables
16
Continuous latent variables
17
Generative adversarial networks
18
Normalizing Flows
19
Autoencoders
20
Diffusion Models
References
Table of contents
17.1
Adversarial training
17.1.1
Loss functions
17.1.2
GAN training in practice
17.2
Image GANs
17.2.1
CycleGAN
17
Generative adversarial networks
a short introduction.
17.1
Adversarial training
17.1.1
Loss functions
17.1.2
GAN training in practice
17.2
Image GANs
17.2.1
CycleGAN
16
Continuous latent variables
18
Normalizing Flows