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How to write the reconstruction loss function in VAE?
In VAE, the reconstruction loss function can be expressed as: reconstruction_loss = – log (p (x | z)) If the decoder output distribution is assumed to be Gaussian, then the loss function boils down to MSE since:
Why do we use MSE as a reconstruction loss?
If we assume a constant variance for the posterior, we naturally end up with the MSE as loss function. An alternative option is proposed by An et al.. We can duplicate the output layer of the decoder to model the mean and variance of the normal distribution and then optimize the negative log likelihood.
How is the VAE loss function defined in keras?
I looked at the Keras documentation and the VAE loss function is defined this way: In this implementation, the reconstruction_loss is multiplied by original_dim, which I don’t see in the first implementation!
When do target and reconstruction are not used in the loss function?
When target and reconstruction are not the used in the loss function, they are never initialized, meaning that z_mean, and z_log_var are not initilized too. When they are not initialized, this results in a None value that causes an error. I’m not sure it’s really this.
When to use r2-loss or MSE-loss?
When having real valued entries (e.g. floats between 0 and 1 as normalized representation for greyscale values from 0 to 256) in our label vector, I always thought that we use MSE (R2-loss) if we want to measure the distance/error between input and output or in general input and label of the network.
What do you need to know about VAE training?
VAEs aim to learn probabilistic representations z of natural languages x, with an objective consisting of two terms: (1) reconstruction to guarantee the inferred latent feature z can represent its corresponding observed sentence; and (2) KL regularization to leverage the prior knowledge to modulate language understanding.