Skip to content

Latest commit

 

History

History
78 lines (53 loc) · 2.38 KB

README.md

File metadata and controls

78 lines (53 loc) · 2.38 KB

Exploring Latent Dimensions of Crowd-sourced Creativity

(Accepted at Machine Learning for Creativity and Design (NeurIPS Workshop), 2021)

Our code is based on the official GANalyze repository

Overview

Requirements and Installation

We have tested our framework and produced the official results with the libraries and their corresponding versions below. However, newer versions will very likely work without a problem. If you experience a problem, don't hesitate to open an issue.

  • CUDA 10.2
  • PyTorch >= 1.6.0 and torchvision >= 0.7.0
  • efficientnet-pytorch
  • numpy, scipy, PIL

One possible way of installing the libraries on a linux server is as follows:

# CUDA 10.2
pip install torch==1.6.0 torchvision==0.7.0

# Efficientnet Pytorch
pip install efficientnet-pytorch==0.7.0

Finally, to clone this repo, run:

git clone https://github.com/catlab-team/latentcreative.git

Training

Before training, pretrained BigGAN models should be downloaded using the script below:

cd GANalyze
sh download_pretrained.sh

After download, you can use the training script below to train the proposed framework:

python train_pytorch.py --assessor_path ../CreativeClassifier/models/best.pth \
--experiment_name experiment --artbreeder_class 0 --class_direction 1 --num_samples 400000 \
--clipped_step_size 1 --transformer AdaptiveDirectionZAdaptiveDirectionY_noise_nonlinear class_reg=1000,noise_dim=8 \
--batch_size 8 --learning_rate 0.0001 --multiway_linear 0

Testing

After training, you can use the testing script below to produce images.

python test_artbreed.py --checkpoint 400000 --checkpoint_dir Checkpoints/experiment/biggan512_None/creative_classifier_best.pth/AdaptiveDirectionZAdaptiveDirectionY_noise_nonlinear_class_reg=1000,noise_dim=8/artbreeder_class:0/[HASH]

Reference

BibTeX reference of our work is as follows:

@article{kocasari2021exploring,
  title={Exploring Latent Dimensions of Crowd-sourced Creativity},
  author={Kocasari, Umut and Bag, Alperen and Atici, Efehan and Yanardag, Pinar},
  journal={arXiv preprint arXiv:2112.06978},
  year={2021}
}