Vanilla VAEs with Gaussian posteriors / priors and factorized pixel distributions aren't blurry, they're noisy. People tend to show the mean value of p(x|z) rather than drawing samples from it. Hence the reported blurry samples aren't actually samples from the model, and they don't reveal...
在 vanilla 变分自动编码器(variational autoencoder,)中，z 通常被视为实数的有限维向量(finite-dimensio...
And still can be utilised as a mixer for different audio signals. Here is aKAN-based VAE model, aKAN-based VQ-VAE modeland a *KAN-based RVQ model. RVQ model seems converge way slower than vanilla VQ-VAE. Maybe the average strategy to update the codebook leads to this slow converge?
Autoencoders are an older neural network architecture that excel at automating the process of representing raw data more efficiently for variousmachine learningand AI applications. Plain, vanilla autoencoders are helpful incodeccreation for compressing data and detecting anomalies. However, they...
It would be nice to know, without all the bells and whistles of weighting terms and Consistency Violation, how well a mixture VAE trained using the vanilla variational objective would perform as a clustering algorithm. Fortunately, the paper does provide such an example in Figure 2d. However, ...
其实可以有两个角度来理解VAE, 分别是生成模型的角度和编码的角度。这两个角度的出发点截然不同,但是最终都回归到统一的 VAE 模型中。 编码角度 首先,我们考虑最简单的编码模型:低秩矩阵分解。 y\approx ABx 其中x,y\in\mathbb{R}^d, A\in\mathbb{R}^{d\times s}, B\in\mathbb{R}^{s\times d}....