Self-supervised speech (SSL) models have recently become widely adopted for
many downstream speech processing tasks. The general usage pattern is to employ
SSL models as feature extractors, and then train a downstream prediction head
to solve a specific task. However, different layers of SSL models have been
shown to capture different types of information, and the methods of combining
them are not well studied. To this end, we extend the general framework for SSL
model utilization by proposing the interface that connects the upstream and
downstream. Under this view, the dominant technique of combining features via a
layerwise weighted sum can be regarded as a specific interface. We propose
several alternative interface designs and demonstrate that the weighted sum
interface is suboptimal for many tasks. In particular, we show that a
convolutional interface whose depth scales logarithmically with the depth of
the upstream model consistently outperforms many other interface designs.

提出了一种连接上游和下游模型的接口，通过使用卷积接口设计，其深度与上游模型的深度按对数比例计算，在许多任务上始终优于其他接口设计。

自我监督语音模型界面设计

Interface Design for Self-Supervised Speech Models

Utility and privacy are two crucial measurements of the quality of synthetic
tabular data. While significant advancements have been made in privacy
measures, generating synthetic samples with high utility remains challenging.
To enhance the utility of synthetic samples, we propose a novel architecture
called the DownStream Feedback Generative Adversarial Network (DSF-GAN). This
approach incorporates feedback from a downstream prediction model during
training to augment the generator's loss function with valuable information.
Thus, DSF-GAN utilizes a downstream prediction task to enhance the utility of
synthetic samples. To evaluate our method, we tested it using two popular
datasets. Our experiments demonstrate improved model performance when training
on synthetic samples generated by DSF-GAN, compared to those generated by the
same GAN architecture without feedback. The evaluation was conducted on the
same validation set comprising real samples. All code and datasets used in this
research will be made openly available for ease of reproduction.

使用一种名为 DSF-GAN 的新型架构，通过从下游预测模型获得反馈信息来增强生成器的损失函数，从而提高合成样本的效用。与没有反馈的相同类型的 GAN 生成的合成样本相比，DSF-GAN 生成的合成样本在模型性能上表现出更好的效果。

DSF-GAN: 下游反馈生成对抗网络

DSF-GAN: DownStream Feedback Generative Adversarial Network

Recently there has been a significant interest in learning disentangled
representations, as they promise increased interpretability, generalization to
unseen scenarios and faster learning on downstream tasks. In this paper, we
investigate the usefulness of different notions of disentanglement for
improving the fairness of downstream prediction tasks based on representations.
We consider the setting where the goal is to predict a target variable based on
the learned representation of high-dimensional observations (such as images)
that depend on both the target variable and an \emph{unobserved} sensitive
variable. We show that in this setting both the optimal and empirical
predictions can be unfair, even if the target variable and the sensitive
variable are independent. Analyzing the representations of more than
\num{12600} trained state-of-the-art disentangled models, we observe that
several disentanglement scores are consistently correlated with increased
fairness, suggesting that disentanglement may be a useful property to encourage
fairness when sensitive variables are not observed.

本篇论文探讨了如何利用不同的解缠绕概念提高表示学习中多元回归模型的公平性，并证明在某些情况下，最优的和经验的预测结果都可能是不公平的。