论文标题
变分超编码网络
Variational Hyper-Encoding Networks
论文作者
论文摘要
我们提出了一个称为HyperVae的框架，用于编码分布的分布。当目标分布由VAE建模时，其神经网络参数θ是从分布p（θ）绘制的，该分布由高级VAE建模。我们提出了使用高斯混合模型的变异推断，以隐式编码参数θInto低维高斯分布。给定目标分布，我们预测潜在代码的后验分布，然后使用矩阵网格解码器生成后验分布q（θ）。与通用的超网络实践相比，HyperVae可以编码参数θin的完整，该实践仅生成比例和偏置向量作为目标网络参数。因此，Hypervae保留了有关潜在空间中每个任务的模型的更多信息。我们使用最小描述长度（MDL）原理讨论了Hypervae，并表明它有助于Hypervae概括。我们评估了密度估计任务，新型设计类别的离群检测和发现，以证明其功效。
We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters θis drawn from a distribution p(θ) which is modeled by a hyper-level VAE. We propose a variational inference using Gaussian mixture models to implicitly encode the parameters θinto a low dimensional Gaussian distribution. Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(θ). HyperVAE can encode the parameters θin full in contrast to common hyper-networks practices, which generate only the scale and bias vectors as target-network parameters. Thus HyperVAE preserves much more information about the model for each task in the latent space. We discuss HyperVAE using the minimum description length (MDL) principle and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density estimation tasks, outlier detection and discovery of novel design classes, demonstrating its efficacy.
