(x_start, model_mean, 0.5 * model_log_variance) decoder_nll = torch.mean(decoder_nll, dim=[1, 2, 3]) / np.log(2.0) #如果t=0就计算负对数似然,否则就计算kl散度 vlb_loss = torch.where((t == 0), decoder_nll, kl) # reweight VLB(添加上系数) vlb_loss *= self.timesteps / ...
:param t: the value of t, starting at 0 for the first diffusion step. :param clip_denoised: if True, clip the x_start prediction to [-1, 1]. :param denoised_fn: if not None, a function which applies to the x_start prediction before it is used to sample. :param model_kwargs:...
Based on the influence factors of emergency diffusion and supply chain structure in uncertain environment, this paper studies the diffusion effect of emergency and establishes an improved Bass diffusion model. On this basis, information diffusion simulation is carried out. Finally, management suggestions ...
The KD-VGG and KD-UNet modules were introduced for initial feature extraction, and the improved implicit diffusion model (IIDM) was proposed. The results showed: (1) The VGG module improved initial feature extraction, improving accuracy, and reducing inference time with optimized model parameters....
This section of the README walks through how to train and sample from a model. Installation Clone this repository and navigate to it in your terminal. Then run: pip install -e . This should install theimproved_diffusionpython package that the scripts depend on. ...
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find ...
improved-diffusion This is the codebase for Improved Denoising Diffusion Probabilistic Models. Usage This section of the README walks through how to train and sample from a model. Installation Clone this repository and navigate to it in your terminal. Then run: pip install -e . This should ...
Orientation probability density transform (OPDT) is a single-shell high angular resolution diffusion imaging (HARDI) estimator proposed by Tristán-Vega et al. for orientation probability density function (OPDF), i.e. marginal orientation distribution function (ODF) of white matter fibers in the bra...
We explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropic domain. Based on this representation, two approaches to white matter filtering are tested, and their performance is evaluated on both semi-synthetic phantoms and ...
本图说明,在无穷的diffusion的步骤后,对于方差\sigma_t的选择可能对于样本的质量完全没有影响,也就意味着,当我们增加diffusion的步骤之后,模型的均值参数影响要大于方差参数。 但是,以上的结论仅仅考虑的样本质量,本文主要考虑的是log-likelihood(众数把握)的情况,如下图 ...