Centred Kernel Alignment (CKA) has recently emerged as a popular metric to
compare activations from biological and artificial neural networks (ANNs) in
order to quantify the alignment between internal representations derived from
stimuli sets (e.g. images, text, video) that are presented to both systems. In
this paper we highlight issues that the community should take into account if
using CKA as an alignment metric with neural data. Neural data are in the
low-data high-dimensionality domain, which is one of the cases where (biased)
CKA results in high similarity scores even for pairs of random matrices. Using
fMRI and MEG data from the THINGS project, we show that if biased CKA is
applied to representations of different sizes in the low-data
high-dimensionality domain, they are not directly comparable due to biased
CKA's sensitivity to differing feature-sample ratios and not stimuli-driven
responses. This situation can arise both when comparing a pre-selected area of
interest (e.g. ROI) to multiple ANN layers, as well as when determining to
which ANN layer multiple regions of interest (ROIs) / sensor groups of
different dimensionality are most similar. We show that biased CKA can be
artificially driven to its maximum value when using independent random data of
different sample-feature ratios. We further show that shuffling sample-feature
pairs of real neural data does not drastically alter biased CKA similarity in
comparison to unshuffled data, indicating an undesirable lack of sensitivity to
stimuli-driven neural responses. Positive alignment of true stimuli-driven
responses is only achieved by using debiased CKA. Lastly, we report findings
that suggest biased CKA is sensitive to the inherent structure of neural data,
only differing from shuffled data when debiased CKA detects stimuli-driven
alignment.

利用 fMRI 和 MEG 数据，本文揭示了如果在低数据高维度领域中使用偏向 CKA 作为对齐度量标准，则它们由于偏向 CKA 对不同特征 - 样本比率的敏感性而无法直接比较，结果将高度相似，甚至对于随机矩阵对。只有使用去偏 CKA 才能实现真正的刺激驱动响应的正向对齐，同时表明偏向 CKA 对神经数据的内在结构敏感。

修正生物和人工神经网络中偏差的中心核对齐度测量

Correcting Biased Centered Kernel Alignment Measures in Biological and  Artificial Neural Networks

Variational autoencoders (VAEs) are used for transfer learning across various
research domains such as music generation or medical image analysis. However,
there is no principled way to assess before transfer which components to
retrain or whether transfer learning is likely to help on a target task. We
propose to explore this question through the lens of representational
similarity. Specifically, using Centred Kernel Alignment (CKA) to evaluate the
similarity of VAEs trained on different datasets, we show that encoders'
representations are generic but decoders' specific. Based on these insights, we
discuss the implications for selecting which components of a VAE to retrain and
propose a method to visually assess whether transfer learning is likely to help
on classification tasks.

基于 Centred Kernel Alignment 评估 VAEs 在不同数据集上训练的相似性，研究表明 VAEs 的表示是通用的，但解码器是特定的，提出选择 VAE 中重新训练的组件和可视化评估传递学习是否有用于分类任务的方法。