VAE Overfitting
Challenge 3: VAEs Are Prone to Overfitting
When the reconstruction term dominates the VAE loss, the model can collapse toward a vanilla autoencoder and lose the latent-space regularity we worked so hard to get. This is sometimes called posterior collapse: the encoder learns to ignore the latent distribution entirely and the decoder learns to reconstruct without it.
Mitigation: Proper regularization weighting
Tune the KL divergence coefficient carefully. Use early stopping when reconstruction quality on a held-out set starts degrading. Consider β-VAE, which adds an explicit coefficient β > 1 to the KL term, forcing greater disentanglement and latent-space regularity at the cost of some reconstruction quality. Monitor both terms of the loss separately during training, not just the total.