Generative models (GAN, VAE, DDPM, and Transformers) trained in one and two-dimensional data. The point of this is simple: lowering the dimensionality of the training data will aid newcomers in the field to better understand the mechanics of what these networks do: in particular, what they learn. Whenever some work is inspired by previous work, it will be duly credited.
For now, this project will be developed using PyTorch,
but if possible/I have both time and patience, I will expand it to other frameworks
such as Keras/TensorFlow 2.0, Julia, etc. (this is my way of saying I will
accept pull requests for anyone interested in contributing).
- Complete 1D GAN code (the following is not necessarily in order and some
may even overlap)
- Make
train.pyandgenerate.pycode - Add manual seed to latents to easily sample from and create interpolations and whatnot (in both scripts)
- Add command-line arguments (make most things controllable, but with
default values); use
clickfor this - Add samples of training results to README (distribution plots, videos)
- Make the neural networks easily editable using
config.yml
- Make
- Ibidem for VAE, but not as pressing
- Ibidem for DDPM
- Ibidem for Transformer
- Add/update
requirements.txt