mijanr/Synthesis-Studio

GANs, AEs, and VAEs for generating synthetic images

Synthesis-Studio: GANs, AEs, and VAEs for generating synthetic images

This repository contains the implementation of various generative models, e.g., GANs, VAEs, etc.

GANs

GANs folder contains the implementation of various GANs, e.g., DCGAN, cGAN, AcGAN, LS-GAN etc. src folder contains the implementation of the GANs, and notebooks folder contains the notebooks used to train and test the models.

Implementations

• Conditional DCGAN (cDCGAN) | Notebook
• Auxiliary Classifier GAN (ACGAN)
• Conditional GAN (cGAN) | Notebook
• Vanilla GAN | Notebook
• Least Squares GAN (LS-GAN)
• VAE-GAN | Notebook

Results

Here are some of the results of the GANs implemented in this repository:

• Conditional DCGAN

  The title shows the label of the generated image, and the image is the generated image.

  

AE_VAEs

AE_VAEs folder contains the implementation of various Autoencoders and Variational Autoencoders. src folder contains the implementation of the models, and notebooks folder contains the notebooks used to train and test the models.

Implementations

• Autoencoder | Notebook
• Denoising Autoencoder (DAE) | Notebook
• Variational Autoencoder (VAE) | Notebook
• Conditional Variational Autoencoder (cVAE) | Notebook

Results

Here are some of the results of the AE_VAEs implemented in this repository:

• Autoencoder

  The image shows the original and the reconstructed digit from the MNIST dataset.

  

• Conditional VAE

  The image titile shows the label of the generated image, and the image is the generated image.

  

References

• Understanding AE&VAE:
  • https://www.jeremyjordan.me/variational-autoencoders/
  • https://www.jeremyjordan.me/autoencoders/
  • https://www.simplilearn.com/tutorials/deep-learning-tutorial/what-are-autoencoders-in-deep-learning
• Understanding GANs:
  • https://developers.google.com/machine-learning/gan/gan_structure
  • https://neptune.ai/blog/6-gan-architectures
• Understanding Transformers:
  • https://cgarbin.github.io/understanding-transformers-in-one-morning/
  • https://e2eml.school/transformers.html
• Understanding Diffusion Models:
  • https://encord.com/blog/diffusion-models/#:~:text=Diffusion%20models%20are%20a%20class%20of%20generative%20models%20that%20simulate,a%20sequence%20of%20invertible%20operations
  • https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
  • https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/

Requirements

requirements.yml file contains the list of all the required libraries to run the code in this repository.

To install the required libraries, run the following command:

conda env create -f requirements.yml