UppuluriKalyani/ML-Nexus

Add GAN Model for Image Generation using CIFAR-10 Dataset

Closed this issue · 2 comments

Kaibalya27 commented

Is your feature request related to a problem? Please describe.

Currently, the repository lacks a Generative Adversarial Network (GAN) for image generation based on the CIFAR-10 dataset. A GAN would enable the model to create synthetic images resembling the CIFAR-10 classes, contributing to the dataset’s augmentation and better generalization in related tasks.

Describe the solution you'd like

Implement a GAN model that consists of:

  • Generator: Takes random noise and generates images based on CIFAR-10.
  • Discriminator: Classifies real CIFAR-10 images from the generated ones.
  • The two networks will be trained adversarially, where the Generator aims to fool the Discriminator, and the Discriminator learns to differentiate real and fake images.
  • Ensure the model produces realistic images after a few epochs of training.

Additional context

This feature could be integrated into an existing folder for models or under a new folder dedicated to generative models.
Screenshots and sample images of generated results would be included for better visualization of the GAN performance.

github-actions commented

Thanks for creating the issue in ML-Nexus!🎉
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    Tag @Neilblaze,@SaiNivedh26 for assigning the issue to you.
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github-actions commented

Hello @Kaibalya27! Your issue #505 has been closed. Thank you for your contribution!