Autoencoders, VAEs, and GANs on Digits (stochastic.blog)

A recent exploration into autoencoders, variational autoencoders (VAEs), and generative adversarial networks (GANs) was conducted using the MNIST dataset, focusing on how much information can be lost while still successfully reconstructing digit images. The key takeaway is a deep dive into compression; each model compresses digits into small latent codes before reconstructing them, highlighting the intricate balance between the fidelity of reconstruction and the size of the latent representation. The plain autoencoder achieved a validation mean squared error (MSE) of 0.06489 per pixel, outperforming a mean-image baseline, indicating the complexities of image generation despite the seemingly simple task of digit reconstruction. The study is significant as it evaluates different variations of autoencoders, including a denoising version that learns to reconstruct images from noisy inputs, and a sparse variant that imposes a penalty on the latent values for better compression. Notably, while the denoising model did not yield a clear accuracy advantage, it demonstrated resilience to noise. In contrast, the sparse autoencoder effectively reduced the mean absolute latent value, signaling efficient resource usage in the compression process. This experimentation not only underscores the challenges involved in generative modeling but also serves as a practical guide for researchers seeking to understand the trade-offs in reconstruction quality and model complexity in AI/ML.