2019年5月21日 · 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 …

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

2018年3月7日 · 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 …

2020年9月1日 · 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 …

2022年11月8日 · the problem would now be when we optimize for the vq-vae loss (the second term of our loss). it would get larger and larger and the embeddings would always try to chase …

2020年4月30日 · Since, we have a Gaussian prior, reconstruction loss becomes the squared difference(L2 distance) between input and reconstruction.(logarithm of gaussian reduces to …

2020年6月20日 · The VAE loss is composed of two terms: Reconstruction loss KLD loss in the implementation there is -0.5 applied to KLD loss. Kindly let me know what is this -0.5

2020年5月6日 · Since, we have a Gaussian prior, reconstruction loss becomes the squared difference(L2 distance) between input and reconstruction.(logarithm of gaussian reduces to …

$\beta$-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 …