Causal mapping of the functional organisation of the human brain requires
evidence of \textit{necessity} available at adequate scale only from
pathological lesions of natural origin. This demands inferential models with
sufficient flexibility to capture both the observable distribution of
pathological damage and the unobserved distribution of the neural substrate.
Current model frameworks -- both mass-univariate and multivariate -- either
ignore distributed lesion-deficit relations or do not model them explicitly,
relying on featurization incidental to a predictive task. Here we initiate the
application of deep generative neural network architectures to the task of
lesion-deficit inference, formulating it as the estimation of an expressive
hierarchical model of the joint lesion and deficit distributions conditioned on
a latent neural substrate. We implement such deep lesion deficit inference with
variational convolutional volumetric auto-encoders. We introduce a
comprehensive framework for lesion-deficit model comparison, incorporating
diverse candidate substrates, forms of substrate interactions, sample sizes,
noise corruption, and population heterogeneity. Drawing on 5500 volume images
of ischaemic stroke, we show that our model outperforms established methods by
a substantial margin across all simulation scenarios, including comparatively
small-scale and noisy data regimes. Our analysis justifies the widespread
adoption of this approach, for which we provide an open source implementation:
this https URL

本研究采用深度生成神经网络架构进行损伤缺陷推断的任务，并与现有方法进行比较。结果表明，该模型在所有情况下均显著优于已有方法，包括相对较小和嘈杂的数据情况。

深度变分损伤缺陷映射

Deep Variational Lesion-Deficit Mapping

The phase retrieval problem asks to recover a natural signal $y_0 \in
\mathbb{R}^n$ from $m$ quadratic observations, where $m$ is to be minimized. As
is common in many imaging problems, natural signals are considered sparse with
respect to a known basis, and the generic sparsity prior is enforced via
$\ell_1$ regularization. While successful in the realm of linear inverse
problems, such $\ell_1$ methods have encountered possibly fundamental
limitations, as no computationally efficient algorithm for phase retrieval of a
$k$-sparse signal has been proven to succeed with fewer than $O(k^2\log n)$
generic measurements, exceeding the theoretical optimum of $O(k \log n)$. In
this paper, we propose a novel framework for phase retrieval by 1) modeling
natural signals as being in the range of a deep generative neural network $G :
\mathbb{R}^k \rightarrow \mathbb{R}^n$ and 2) enforcing this prior directly by
optimizing an empirical risk objective over the domain of the generator. Our
formulation has provably favorable global geometry for gradient methods, as
soon as $m = O(kd^2\log n)$, where $d$ is the depth of the network.
Specifically, when suitable deterministic conditions on the generator and
measurement matrix are met, we construct a descent direction for any point
outside of a small neighborhood around the unique global minimizer and its
negative multiple, and show that such conditions hold with high probability
under Gaussian ensembles of multilayer fully-connected generator networks and
measurement matrices. This formulation for structured phase retrieval thus has
two advantages over sparsity based methods: 1) deep generative priors can more
tightly represent natural signals and 2) information theoretically optimal
sample complexity. We corroborate these results with experiments showing that
exploiting generative models in phase retrieval tasks outperforms sparse phase
retrieval methods.

本篇论文提出了一种新的相位恢复框架，利用深度生成神经网络建模自然信号，并通过优化经验风险目标来强制执行这个先验，该方法相较于稀疏基于的方法有两个优点：1）深度生成先验可以更紧密地表示自然信号，2）信息熵分析了样本复杂度。

基于生成先验的相位恢复

Phase Retrieval Under a Generative Prior

This paper presents Generative Adversarial Talking Head (GATH), a novel deep
generative neural network that enables fully automatic facial expression
synthesis of an arbitrary portrait with continuous action unit (AU)
coefficients. Specifically, our model directly manipulates image pixels to make
the unseen subject in the still photo express various emotions controlled by
values of facial AU coefficients, while maintaining her personal
characteristics, such as facial geometry, skin color and hair style, as well as
the original surrounding background. In contrast to prior work, GATH is purely
data-driven and it requires neither a statistical face model nor image
processing tricks to enact facial deformations. Additionally, our model is
trained from unpaired data, where the input image, with its auxiliary identity
label taken from abundance of still photos in the wild, and the target frame
are from different persons. In order to effectively learn such model, we
propose a novel weakly supervised adversarial learning framework that consists
of a generator, a discriminator, a classifier and an action unit estimator. Our
work gives rise to template-and-target-free expression editing, where still
faces can be effortlessly animated with arbitrary AU coefficients provided by
the user.

本文提出了一种名为 GATH 的新型深度生成神经网络，利用动作单元系数实现任意肖像的完全自动面部表情合成。与以往工作不同，GATH 仅仅需要数据驱动，无需人脸模型或图像处理技巧实现面部变形。此外，我们还提出了一种弱监督对抗学习框架，通过生成器、判别器、分类器和动作单元估计器有效地学习该模型，从而实现了无模板目标的表情编辑。