Project V: Generating Images with the Variational Auto-Encoder
Project Steps:
- Data Collection: Download and prepare the CelebA Faces dataset, ensuring a collection of celebrity face images for training the VAE model.
- Data Preprocessing: Resize images if necessary, normalize pixel values, and split the data into training and testing sets.
- VAE Architecture Creation: Define the architecture of the Variational Autoencoder, including the encoder and decoder components. The encoder compresses input images into latent representations, while the decoder generates new images from these representations.
- Loss Function Definition: Establish a custom loss function for the VAE, incorporating both reconstruction loss and Kullback-Leibler (KL) divergence loss. The reconstruction loss measures image generation accuracy, while the KL loss encourages latent representations to follow a normal distribution.
- VAE Training: Train the VAE model on the CelebA Faces training dataset. Monitor loss during training and adjust hyperparameters if necessary.
- Image Generation: Once the VAE model is trained, utilize it to generate new celebrity face images. Sample points from the latent space to produce different faces.
- Evaluation: Assess the quality of the generated images using metrics such as Structural Similarity Index (SSIM) or visual evaluations.
- Results Display: Display the generated celebrity faces produced by the model. Create an image grid to visualize the results.
- Optimization and Iterations: Iterate on training, architecture, and hyperparameters to enhance the quality of generated images.
- CelebA Faces Dataset: The CelebA Faces dataset is renowned for facial recognition and celebrity image generation tasks. It comprises a vast collection of high-resolution celebrity face images from various sources, making it a valuable resource for diverse image generation. Key features of the CelebA Faces dataset include its extensive size, diverse facial characteristics, annotations accompanying each image, and images typically being high-resolution and in JPG format.
