Add GAN Model for Image Generation using CIFAR-10 Dataset
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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.
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Hello @Kaibalya27! Your issue #505 has been closed. Thank you for your contribution!