Disentangled Latent Spaces VAE vs Beta VAE

The project investigates how scaling the KL-divergence term with a β-weight encourages the model to learn factorized and interpretable latent dimensions. Through extensive experiments, the study demonstrates how β-VAE isolates high-level semantic attributes—such as smile, gender, background texture, facial orientation, and illumination—more effectively than a standard VAE.