Role of Kullback-Leibler (KL) Divergence in the Variational Autoencoder Loss Function
The Kullback-Leibler (KL) divergence acts as a regularization term in the loss function of a Variational Autoencoder (VAE). It improves the model in two main ways: first, it ensures that the learned latent space follows a well-defined probability distribution; second, it reduces the probability of gap formation between point clusters in the latent space, encouraging continuous and meaningful representations. The total loss is calculated by adding the reconstruction loss and the KL divergence loss. A weight factor is often applied to the reconstruction loss to balance the two components, as poorly choosing this weight term can lead to unsatisfactory results.
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