Speech models may be affected by performance imbalance in different
population subgroups, raising concerns about fair treatment across these
groups. Prior attempts to mitigate unfairness either focus on user-defined
subgroups, potentially overlooking other affected subgroups, or do not
explicitly improve the internal representation at the subgroup level. This
paper proposes the first adoption of contrastive learning to mitigate speech
model bias in underperforming subgroups. We employ a three-level learning
technique that guides the model in focusing on different scopes for the
contrastive loss, i.e., task, subgroup, and the errors within subgroups. The
experiments on two spoken language understanding datasets and two languages
demonstrate that our approach improves internal subgroup representations, thus
reducing model bias and enhancing performance.

使用对比学习技术来减轻语音模型中的偏差，改善性能不佳的子群体的内部表示，从而降低模型偏差并提升性能。

缓解语音模型中的偏见的对比学习方法

A Contrastive Learning Approach to Mitigate Bias in Speech Models

Indoor monocular depth estimation has attracted increasing research interest.
Most previous works have been focusing on methodology, primarily experimenting
with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall
performance over the test set. However, little is known regarding robustness
and generalization when it comes to applying monocular depth estimation methods
to real-world scenarios where highly varying and diverse functional
\textit{space types} are present such as library or kitchen. A study for
performance breakdown into space types is essential to realize a pretrained
model's performance variance. To facilitate our investigation for robustness
and address limitations of previous works, we collect InSpaceType, a
high-quality and high-resolution RGBD dataset for general indoor environments.
We benchmark 11 recent methods on InSpaceType and find they severely suffer
from performance imbalance concerning space types, which reveals their
underlying bias. We extend our analysis to 4 other datasets, 3 mitigation
approaches, and the ability to generalize to unseen space types. Our work marks
the first in-depth investigation of performance imbalance across space types
for indoor monocular depth estimation, drawing attention to potential safety
concerns for model deployment without considering space types, and further
shedding light on potential ways to improve robustness. See
https://depthcomputation.github.io/DepthPublic for data.

室内单目深度估计的鲁棒性和泛化性研究，通过对不同空间类型的性能不平衡进行深入调查和分析，为室内单目深度估计的模型部署提供可行的解决方案。