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 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

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

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 the reconstruction loss and whether it is over the entire image (sum of squared differences) or per pixel (average sum of squared differences).

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 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

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 the encoder outputs towards $-\infty$ and $+\infty$, thus creating an instable cycle, effectively ruining the training process.

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 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 ...

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 squared difference). In the paper, authors are trying to get intuition from probabilistic PCA to explain when the posterior collapse happens. pPCA model,trained EM or gradeint ascent ...

$\beta$-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: