Unsupervised GANs
Experiments done with Unsupervised GANs for my Master's Degree dissertation.
Objective
The objective of the experiment is to create generators capable of generating a latent vector that can be used for image manipulation.
Experiment Page
The complete experiment can be accessed on (PT-BR only):
https://www.notion.so/vinitrevisan/N-o-Supervisionada-cc97dd35ef954aed8023bfe0b265853b
Contents
main.py
File with the experiment core. Training, test and validation of the networks. Parameters of the experiment.
networks.py
File with the classes and functions to create the generators and discriminators used on the experiments.
losses.py
File with the functions used to evaluate the losses to train the GANs
utils.py
File with all utilities functions, such as plot control, image processing, and exception handling.
transferlearning.py
File with the functions used to train networks using the Transfer Learning approach.
validate.py
Tests vector interpolation to see how the reconstruction of interpolated images is working for a given generator.
Also part of this project
Autoencoders (github)
Pix2Pix-CycleGAN (github)
Unsupervised-GANs (github)
Main experiment page (Notion [PT-BR])