Authors: Tim Whiting & Evan Peterson
An implementation of a Wasserstein Generative Adversarial Network (WGAN) in Julia for Advanced Machine Learning class.
Needed dependencies for running the project locally (run this from the Julia REPL):
import Pkg; Pkg.add.(["Flux", "Images", "ImageMagick", "NNlib", "BSON", "Plots", "Juno", "FileIO"])If running on GPU with your local machine, also include:
import Pkg; Pkg.add.(["CuArrays"])Here is a link to the JuliaTPU repository which tells how to run compile Julia to run on TPU's and gives instructions how to do it on google's colab: https://github.com/JuliaTPU/XLA.jl
Get the datasets from here: https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/ Clone this python repository that converts them all to images: https://github.com/ndrplz/small_norb Follow the instructions in the repository to convert all of the files to images
- Run for a long time, see if works
- Implement Experiment(s), Options:
- Bidirectional latent encoder
- Coordinate convolutions
- Validate on a hold-out set from the same distribution as the train set
- Anything else cool!