GAN Flashcards
What are generative models
Unsupervised method to build a model explaining data. Given training data they generate new instances extracted from modeled distribution
When training GAN the density estimation is explicitly known T F
F
GAN principle
Model distribution is learnt from training distribution to generate data through competitive two player game
Discriminator structure in GAN
Encoder (AE or CNN) and classifier (logistic neuron)
Gan schema
We get real sample from dataset and a generated sample by the generator which is fed with a random variable. The two samples are alternatively introduced in the net of the discriminator which find who is fake
The generator network of a Gan acts as a …
DECODER
Guidelines for GAN generator architecture
Generator is an upsampling network which implements deep deconvolution.
Guideline for discriminator archietecture in Gan
Use convolutional network performing classification. Some authors proposed also Fullyconnected FF
Goal of the generator network in Gan
Try to fool the discriminator by producing real-looking data
Role of the discriminator
Try to distinguish between real and fake data
Gan principle of training
The generator must be able to successfully trick the discriminator so that we are generating data that look like data from the training set
Training approach of GAN
Estimating jointly teta-d e teta-g in the so-called minimax game
Possible tasks for GAN
Generating images, image translation
How to generate images with a query?
Couple LLM and GAN
How GAN can be used in biomedicine?
Synthetic data generation for augmentation in training setup
Image tot image translation
Protein and gene synthetic data bank