What is feature disentanglement in deep vision representations?

Feature disentanglement separates independent factors of variation in learned representations.

In vision, images contain multiple factors like shape, texture, lighting, and pose. Disentangled representations aim to encode each factor independently, improving interpretability and robustness. This is often achieved using variational autoencoders (VAEs), adversarial training, or structured latent spaces.