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https://github.com/breandan/hgan

Hyper volume maximization for GAN training

https://github.com/breandan/hgan

Science Score: 23.0%

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Repository

Hyper volume maximization for GAN training

Basic Info
  • Host: GitHub
  • Owner: breandan
  • Language: Python
  • Default Branch: master
  • Size: 107 KB
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
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Created about 8 years ago · Last pushed about 8 years ago
Metadata Files
Readme

README.md

Hyper Volume Generative Adversarial Network - hGAN

Replication of Stabilizing GAN Training with Multiple Random Projections and extension including training with multi-objective training via hyper volume maximization

To run

Download the cropped and aligned version of CelebA and unzip it

python train.py --ndiscriminators 12

optional arguments: -h, --help show this help message and exit --batch-size N input batch size for training (default: 64) --epochs N number of epochs to train (default: 50) --lr LR learning rate (default: 0.0002) --beta1 lambda Adam beta param (default: 0.5) --beta2 lambda Adam beta param (default: 0.999) --ndiscriminators NDISCRIMINATORS Number of discriminators. Default=8 --checkpoint-epoch N epoch to load for checkpointing. If None, training starts from scratch --checkpoint-path Path Path for checkpointing --data-path Path Path to data --workers WORKERS number of data loading workers --seed S random seed (default: 1) --save-every N how many epochs to wait before logging training status. Default is 5 --hyper-mode enables training with hypervolume maximization --nadir-factor nadir Factor of the max disc loss to initialize nadir point (default: 50.0) --no-cuda Disables GPU use

Tested with

  • Python 3.6
  • Pytorch 0.3.0

To do

  • Scheduler for the nadir point

Collaborators: Isabela Albuquerque, Breandan Considine

Owner

  • Name: breandan
  • Login: breandan
  • Kind: user

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