Triple GANs

Deep Generative models(DGM) are neural networks which tries to estimate the likelihood of each observation and to create new samples from the underlying distribution of data, Synthetic data generation is a specific application of deep generative models where the goal is to create realistic and novel data samples. GANs, in particular, consist of a generator network that produces new samples and a discriminator network that distinguishes between real and generated samples. Through an adversarial training process, both networks improve iteratively, with the generator learning to produce more realistic data.

However, challenges exist. Ensuring diversity and realism in generated samples is an ongoing research focus. Issues like training stability, mode collapse, and addressing biases in generated data are important considerations. Ethical concerns regarding the potential misuse of synthetic data and privacy implications must be taken into account. The Triple GAN focuses on classification and class-conditional generation of data samples and also a better framework to work with as it requires less number of epochs to train, can perform well even without data argumentation and can perform semi-supervised learning(SSL) tasks better.

Triple GAN Architecture

This repository contains an implementation of Triple GAN, a generative adversarial network designed to simultaneously improve image generation and classification through a joint training approach.

Overview

Triple GAN consists of three networks:

1. Generator (G) - Generates realistic images from latent vectors.
2. Discriminator (D) - Distinguishes between real and generated images while also serving as a classifier.
3. Classifier (C) - Assigns labels to images and enhances classification performance using adversarial learning.

By balancing the adversarial loss and classification loss, Triple GAN achieves better sample generation and more robust classification than conventional GANs.

Mathematical Formulation

The optimization objective of Triple GAN is formulated as:

$$ \min_{C,G} \max_D E_{p(x,y)} [\log D(x, y)] + \alpha E_{p_c(x,y)} [\log(1 - D(x, y))] + (1 - \alpha) E_{p_g(x,y)} [\log(1 - D(G(y, z), y))] $$

where: where:

• $p(x, y)$ represents the true data distribution,
• $p_c(x, y)$ is the classifier-based sample distribution,
• $p_g(x, y)$ is the generator-based sample distribution,
• $\alpha$ is a balancing parameter.

Dependencies

Ensure you have the following installed:

• Python 3.x
• TensorFlow / PyTorch
• NumPy
• Matplotlib
• Pandas
• Jupyter Notebook

Install dependencies using:

pip install tensorflow numpy matplotlib pandas jupyter

Usage

1. Clone the repository:

   https://github.com/Rupesh4604/Triple-GAN.git
   cd Triple-GAN-notebook

2. Open the Jupyter Notebook:

   jupyter notebook notebook-triplegan-2.ipynb

3. Run the notebook to train the Triple GAN model and visualize the results.

Architecture

Loss

License

This project is licensed under the MIT License.

Contact

For queries or contributions, contact: rupesh32003@gmail.com