This paper is the first work to propose a network to predict a structured
uncertainty distribution for a synthesized image. Previous approaches have been
mostly limited to predicting diagonal covariance matrices. Our novel model
learns to predict a full Gaussian covariance matrix for each reconstruction,
which permits efficient sampling and likelihood evaluation.
We demonstrate that our model can accurately reconstruct ground truth
correlated residual distributions for synthetic datasets and generate plausible
high frequency samples for real face images. We also illustrate the use of
these predicted covariances for structure preserving image denoising.

本篇论文首次提出了一种网络模型，其可预测合成图像的结构化不确定性分布。模型可学习预测每个重构的完整高斯协方差矩阵，从而实现有效的采样和似然评估，通过该方法，能够准确地重构合成数据集的地面真实相关残差分布，并为真实面部图像生成可信度高的概率密度函数分布。同时，展示了合成图像的结构保留降噪方法。

结构化不确定性预测网络

Structured Uncertainty Prediction Networks

We propose a novel architecture for $k$-shot classification on the Omniglot
dataset. Building on prototypical networks, we extend their architecture to
what we call Gaussian prototypical networks. Prototypical networks learn a map
between images and embedding vectors, and use their clustering for
classification. In our model, a part of the encoder output is interpreted as a
confidence region estimate about the embedding point, and expressed as a
Gaussian covariance matrix. Our network then constructs a direction and class
dependent distance metric on the embedding space, using uncertainties of
individual data points as weights. We show that Gaussian prototypical networks
are a preferred architecture over vanilla prototypical networks with an
equivalent number of parameters. We report state-of-the-art performance in
1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot
5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset.
We explore artificially down-sampling a fraction of images in the training set,
which improves our performance even further. We therefore hypothesize that
Gaussian prototypical networks might perform better in less homogeneous,
noisier datasets, which are commonplace in real world applications.

提出一种新的基于高斯原型网络的 $k$-shot 分类方法，并在 Omniglot 数据集上取得最新的学术成果。