We are interested in learning data-driven representations that can generalize
well, even when trained on inherently biased data. In particular, we face the
case where some attributes (bias) of the data, if learned by the model, can
severely compromise its generalization properties. We tackle this problem
through the lens of information theory, leveraging recent findings for a
differentiable estimation of mutual information. We propose a novel end-to-end
optimization strategy, which simultaneously estimates and minimizes the mutual
information between the learned representation and the data attributes. When
applied on standard benchmarks, our model shows comparable or superior
classification performance with respect to state-of-the-art approaches.
Moreover, our method is general enough to be applicable to the problem of
``algorithmic fairness'', with competitive results.

利用信息论的有关发现，我们提出了一种新的端到端优化策略，该策略同时估计和最小化学习表示和数据属性之间的互信息，通过这种策略，我们的模型在标准基准测试中表现出与最先进的方法相当或优越的分类性能，此方法可应用于问题的 “算法公平性”，并得到了竞争性的结果。

通过互信息反向传播学习无偏表示

Learning Unbiased Representations via Mutual Information Backpropagation

We study data-driven representations for three-dimensional triangle meshes,
which are one of the prevalent objects used to represent 3D geometry. Recent
works have developed models that exploit the intrinsic geometry of manifolds
and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants,
which learn from the local metric tensor via the Laplacian operator. Despite
offering excellent sample complexity and built-in invariances, intrinsic
geometry alone is invariant to isometric deformations, making it unsuitable for
many applications. To overcome this limitation, we propose several upgrades to
GNNs to leverage extrinsic differential geometry properties of
three-dimensional surfaces, increasing its modeling power.
In particular, we propose to exploit the Dirac operator, whose spectrum
detects principal curvature directions --- this is in stark contrast with the
classical Laplace operator, which directly measures mean curvature. We coin the
resulting models \emph{Surface Networks (SN)}. We prove that these models
define shape representations that are stable to deformation and to
discretization, and we demonstrate the efficiency and versatility of SNs on two
challenging tasks: temporal prediction of mesh deformations under non-linear
dynamics and generative models using a variational autoencoder framework with
encoders/decoders given by SNs.

本研究提出了一种名为 Surface Networks（SN）的模型，该模型通过利用三维曲面的外部微分几何属性，如 Dirac 算子的频谱来定义稳定的形状表示，并证明其对变形和离散化具有稳定性，在两项挑战性任务上展示了 SN 的高效性和多功能性，包括使用 SN 构建的变分自编码器进行时间预测。