:param mu: (Tensor) Mean of the latent Gaussian :param logvar: (Tensor) Standard deviation of the latent Gaussian :return:

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Gumbel-softmax trick to sample from Categorical Distribution :param z: (Tensor) Latent Codes [B x D x Q] :return: (Tensor) [B

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Will a single z be enough ti compute the expectation for the loss?? :param mu: (Tensor) Mean of the latent Gaussian :

Will a single z be enough ti compute the expectation for the loss?? :param mu: (Tensor) Mean of the latent Gaussian :

Gumbel-softmax trick to sample from Categorical Distribution :param mu: (Tensor) mean of the latent Gaussian [B x D] :param

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

Will a single z be enough ti compute the expectation for the loss?? :param mu: (Tensor) Mean of the latent Gaussian :

Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D]

:param mu: (Tensor) Mean of the latent Gaussian :param logvar: (Tensor) Standard deviation of the latent Gaussian :return:

Reparameterize the Gamma distribution by the shape augmentation trick. Reference: [1] https://arxiv.org/pdf/1610.05683.pdf

Samples from the latent space and return the corresponding image space map. :param num_samples: (Int) Number of samples

Samples from the latent space and return the corresponding image space map. :param num_samples: (Int) Number of samples

https://stats.stackexchange.com/questions/11646/kullback-leibler-divergence-between-two-gamma-distributions b and d are Gamma shape p

Decorator to handle the deprecation of data_loader from 0.7 :param fn: User defined data loader function :return: A wrapper for the data_

Given an input image x, returns the reconstructed image. Returns only the first reconstructed sample :param x: (Tensor) [B x

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image. Returns only the first reconstructed sample :param x: (Tensor) [B x

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

Given an input image x, returns the reconstructed image :param x: (Tensor) [B x C x H x W] :return: (Tensor) [B x C x H x W]

KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2} :param args: :param kwargs:

Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}

Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}

Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}

Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}

Computes the VAE loss function. KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}