Aim

To study and implement autoencoders for unsupervised representation learning by training both a basic and a denoising autoencoder on the Fashion-MNIST dataset (mapping noisy inputs to clean outputs), and to analyze unsupervised compression and reconstruction performance through reconstruction grids and a 2-D projection of the learned latent space.