We study the intriguing connection between visual data, deep networks, and
the brain. Our method creates a universal channel alignment by using brain
voxel fMRI response prediction as the training objective. We discover that deep
networks, trained with different objectives, share common feature channels
across various models. These channels can be clustered into recurring sets,
corresponding to distinct brain regions, indicating the formation of visual
concepts. Tracing the clusters of channel responses onto the images, we see
semantically meaningful object segments emerge, even without any supervised
decoder. Furthermore, the universal feature alignment and the clustering of
channels produce a picture and quantification of how visual information is
processed through the different network layers, which produces precise
comparisons between the networks.

使用大脑 fMRI 响应预测作为训练目标，我们研究了视觉数据、深度网络和大脑之间的有趣联系，发现通过不同目标训练的深度网络共享通用的特征通道，将这些通道聚类成不同的大脑区域，产生语义相关的物体片段，同时通过通道聚类发现了不同网络层级处理视觉信息的过程，从而实现了网络之间的精确比较。

AlignedCut：脑引导的通用特征空间上的视觉概念发现

AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature  Space

Machine learning model bias can arise from dataset composition: sensitive
features correlated to the learning target disturb the model decision rule and
lead to performance differences along the features. Existing de-biasing work
captures prominent and delicate image features which are traceable in model
latent space, like colors of digits or background of animals. However, using
the latent space is not sufficient to understand all dataset feature
correlations. In this work, we propose a framework to extract feature clusters
in a dataset based on image descriptions, allowing us to capture both subtle
and coarse features of the images. The feature co-occurrence pattern is
formulated and correlation is measured, utilizing a human-in-the-loop for
examination. The analyzed features and correlations are human-interpretable, so
we name the method Common-Sense Bias Discovery (CSBD). Having exposed sensitive
correlations in a dataset, we demonstrate that downstream model bias can be
mitigated by adjusting image sampling weights, without requiring a sensitive
group label supervision. Experiments show that our method discovers novel
biases on multiple classification tasks for two benchmark image datasets, and
the intervention outperforms state-of-the-art unsupervised bias mitigation
methods.

基于图像描述提取特征聚类的框架，发现数据集中的敏感关联性，通过调整图像采样权重减轻下游模型偏差问题。

分类任务的常识偏见发现与缓解

Common-Sense Bias Discovery and Mitigation for Classification Tasks

A successful paradigm in representation learning is to perform
self-supervised pretraining using tasks based on mini-batch statistics (e.g.,
SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these
methods is an overlooked prior to learn features that enable uniform clustering
of the data. While this prior has led to remarkably semantic representations
when pretraining on class-balanced data, such as ImageNet, we demonstrate that
it can hamper performance when pretraining on class-imbalanced data. By moving
away from conventional uniformity priors and instead preferring power-law
distributed feature clusters, we show that one can improve the quality of the
learned representations on real-world class-imbalanced datasets. To demonstrate
this, we develop an extension of the Masked Siamese Networks (MSN) method to
support the use of arbitrary features priors.

通过使用不同于以往的特征先验，可以改善对类别不平衡数据预训练时的表征学习结果，具体应用于 Masked Siamese Networks 方法。