This repository is the official implementation of Convergence of Non-Convex Non-Concave GANs Using Sinkhorn Divergence.
📋 Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials
To install requirements:
conda install --file requirements_conda.txt
pip install -r requirements_pip.txt
📋 Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
There are two options in running experiment using our code:
- Execute the bash script to run preset configs used in the paper:
NOTE : uncomment any of one line from Line 4-8 to run one of configs used in our paper
bash ./runs.sh
- Run the experiment thru
main.py
python main.py \\
--c path_to_config_file.yaml
--train
To watch the experiment, we use Tensorboard watching the experiment directory
tensorboard --logdir ../experiments/runs
We suggest adding
--samples_per_plugin "scalar=0"for more precise recording of the experiment
To evaluate my model on ImageNet, run:
python eval.py --model-file mymodel.pth --benchmark imagenet
📋 Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).
You can download pretrained models here:
- My awesome model trained on ImageNet using parameters x,y,z.
📋 Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable). Alternatively you can have an additional column in your results table with a link to the models.
Our model achieves the following performance on :
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| My awesome model | 85% | 95% |
📋 Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it.
📋 Pick a licence and describe how to contribute to your code repository.