May 21, 2019 · In VAE, why use MSE loss between input x and decoded sample x' from latent distribution? 1 How to Resolve Variational Autoencoder (VAE) Model Collapse in Reconstruction Task Using Sensor Data?

I was studying VAEs and came across the loss function that consists of the KL divergence. $$ \sum_{i=1}^n \sigma^2_i + \mu_i^2 - \log(\sigma_i) - 1 $$ I wanted to intuitively make sense of the KL divergence part of the loss function. It would be great if somebody can help me

Sep 7, 2020 · That said if you are only concerned by MSE loss, indeed it is not the best loss function for "face" reconstruction albeit still works for low dim images (e.g. celeba) Some of the directions in which VAE are being improved are - Using sophisticated prior and posterior (e.g. normalizing flows) and not just normal, using more stochastic nodes ...

Mar 7, 2018 · Note that when using binary cross-entropy loss in a VAE for black and white images, we do not need to weight the KL divergence term, which has been seen in many implementations. Bounded regression (e.g. regression in [0, 1]) - This explains the case of weighting KL divergence when using binary cross-entropy loss for color images

Jan 30, 2022 · Use return MSE_loss + KLDiv_Loss instead. You can show that this is correct by starting from a Gaussian likelihood for your target tgts and manipulating the algebra to obtain the negative log-likelihood, whence MSE is a rescaling.

Apr 30, 2020 · Since, we have a Gaussian prior, reconstruction loss becomes the squared difference(L2 distance) between input and reconstruction.(logarithm of gaussian reduces to squared difference). To get a better understanding of VAE, let's try to derive VAE loss. Our aim is to infer good latents from the observed data. However, there's a vital problem ...

The reconstruction loss for a VAE (see, for example equation 20.77 in The Deep Learning Book) ...

Sep 1, 2020 · I know VAE's loss function consists of the reconstruction loss that compares the original image and reconstruction, as well as the KL loss. However, I'm a bit confused about the reconstruction loss and whether it is over the entire image (sum of squared differences) or per pixel (average sum of squared differences).

$\beta$-VAE variants encourage better disentangling through use of the $\beta$ parameter, which can be used to increase the emphasis on the Kullback–Leibler divergence (KL) in the loss function, i.e. increased disentangling of the latent dimensions but generally worse reconstruction, i.e. the KL part of the loss function becomes:

Jun 15, 2020 · Sometimes a VAE will have the KL divergence swamp any improvement to the reconstruction. In that case, it can help to anneal the weight assigned to the KLD portion of the loss from 0 (KLD is ignored) to 1 (KLD is given full weight and the loss is the ordinary variational lower-bound). Samuel R. Bowman, Luke Vilnis.