metadata
license: mit
language: en
tags:
- autoencoder
- computer-vision
- image-reconstruction
- celeba
- deep-learning
- face-processing
datasets:
- celeba
pipeline_tag: image-to-image
CelebA Autoencoder
Overview
This project implements a Convolutional Autoencoder trained on the CelebA dataset for image compression and reconstruction.
Features
- Learns compressed latent representation of face images
- Reconstructs images from compressed representation
- Evaluated using PSNR and SSIM metrics
Dataset
Model
- Encoder: Convolutional layers with downsampling
- Decoder: Transposed convolution layers for reconstruction
Results
- Average PSNR: 31.126471439997356
- Average SSIM: 0.9329655667146047
Notes
- Model performs lossy compression
- Some blurring is expected due to reconstruction loss