Project Title : GANs for Image Generation

Objective:
To build a system using Generative Adversarial Networks (GANs) that can generate realistic images from random noise, mimicking real-world data like human faces, animals, or scenery.

What are GANs?
GANs (Generative Adversarial Networks) are a type of deep learning model with two networks:

Generator: Creates fake images.

Discriminator: Tries to distinguish between real and fake images. They train together in a competition until the generator gets good at fooling the discriminator.

Key Concepts:

Unsupervised learning.

Image generation from noise (latent space).

Deep learning architectures (CNNs).

Steps Involved:

Dataset Collection:

Use datasets like MNIST, CIFAR-10, CelebA, LSUN, or any image dataset depending on your goal.

Preprocessing:

Resize and normalize images.

Prepare batches for training (usually 64x64 or 128x128 pixel images).

Model Design:

Generator: Uses transposed convolutions to create images from random noise.

Discriminator: Uses CNN layers to classify real vs fake.

Train using adversarial loss (binary cross-entropy).

Training:

Alternate training both models: the generator gets better at creating images, and the discriminator gets better at detecting fakes.

After training, the generator can produce realistic-looking images from random inputs.

Evaluation:

Visual inspection of generated images.

Use metrics like Inception Score (IS) or Frechet Inception Distance (FID) for quality evaluation.

Applications:

Creating realistic art, faces, or scenery.

Data augmentation for training ML models.

Style transfer, super-resolution, and image-to-image translation.

Game and film content generation.

Tools & Technologies:

Languages: Python

Libraries: TensorFlow/Keras, PyTorch, NumPy, Matplotlib

Extras: Google Colab for GPU support, OpenCV for image handling