Reparameterize a sample eps ~ N(0, 1) so that h(z) ~ Gamma(alpha, 1) :param alpha: (Tensor) Shape parameter :param eps: (Tens

Inverse reparameterize the given z into eps. :param alpha: (Tensor) :param z: (Tensor) :return: (Tensor)

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

:param model: :return:

Permutes each of the latent codes in the batch :param z: [B x D] :return: [B x D]

: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

:param gen: 生成模型 :param val_loader:加载的验证集数据集 :param epoch: 迭代次数 :param folder: 保存文件路径 :return:

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

Initialize the weight to which we will do the exponential moving average and the dictionary where we store the model paramete