https://github.com/ram81/ac-vaegan-pytorch

Implementation of a Conditional Variational Auto-Encoder GAN in pytorch
https://github.com/ram81/ac-vaegan-pytorch

Last synced: 3 months ago
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Implementation of a Conditional Variational Auto-Encoder GAN in pytorch

• Host: GitHub
• URL: https://github.com/ram81/ac-vaegan-pytorch
• Owner: Ram81
• Created: 2019-10-02T06:53:04.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-21T22:32:44.000Z (over 3 years ago)
• Last Synced: 2025-03-17T20:43:45.973Z (3 months ago)
• Language: Python
• Homepage:
• Size: 20.5 KB
• Stars: 43
• Watchers: 2
• Forks: 6
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# ACVAEGAN

An approach of imposing a condition on VAEGAN through the use of an auxiliary classifier.

## Description

- Goal: To build a Conditional VAEGAN by employing an Auxiliary Classifier.

- Architecture:

![ACVAEGAN Architecture](https://github.com/pranavbudhwant/acvaegan/blob/master/architecture.jpg)

- Dataset: WikiArt Emotions[1] 

- Approaches: 

  1. Generate paintings conditioned on emotion (anger, fear, sadness, ..)

  2. Generate paintings conditioned on category (cubism, surrealism, minimalism, ..)

  3. Generate paintings conditioned on style (contemporary, modern, renaissance, ..)

  

## Plan

- ~Prepare dataset for the three approaches~

  - ~CSV files containing (image-id, emotion); (image-id, category); (image-id, style)~

- Auxiliary Classifier Architecture

  - Multilabel Classifier/Multiclass Classifier?

- Keras, Pytorch implementation

- Try on MNIST

 

## References

[1] WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art. Saif M. Mohammad and Svetlana Kiritchenko. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018), May 2018, Miyazaki, Japan.

[2] Autoencoding beyond pixels using a learned similarity metric https://arxiv.org/abs/1512.09300

[3] Conditional Image Synthesis With Auxiliary Classifier GANs https://arxiv.org/abs/1610.09585

[4] Twin Auxiliary Classifiers GAN https://arxiv.org/abs/1907.02690

[5] The Emotional GAN: Priming AdversarialGeneration of Art with Emotion https://nips2017creativity.github.io/doc/The_Emotional_GAN.pdf

[6] CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training https://arxiv.org/pdf/1703.10155.pdf

[7] Learning Structured Output Representationusing Deep Conditional Generative Models https://pdfs.semanticscholar.org/3f25/e17eb717e5894e0404ea634451332f85d287.pdf