Why has the cross-entropy become the classification standard loss function and not Kullback-Leibler divergence?
Why is the Jensen-Shannon divergence preferred over the KL divergence in measuring the performance of a generative network?
How is this statement from a TensorFlow implementation of a certain KL-divergence formula related to the corresponding formula?
What is the impact of scaling the KL divergence and reconstruction loss in the VAE objective function?
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