Aim

To implement and study a basic autoencoder and a denoising autoencoder for unsupervised representation learning on the Fashion-MNIST dataset. The basic autoencoder reconstructs clean images from clean inputs, while the denoising autoencoder reconstructs clean images from noisy inputs. The experiment also compares their compression, reconstruction quality, robustness to noise, and learned 2-D latent representations.