The generalization of machine learning (ML) models to out-of-distribution
(OOD) examples remains a key challenge in extracting information from upcoming
astronomical surveys. Interpretability approaches are a natural way to gain
insights into the OOD generalization problem. We use Centered Kernel Alignment
(CKA), a similarity measure metric of neural network representations, to
examine the relationship between representation similarity and performance of
pre-trained Convolutional Neural Networks (CNNs) on the CAMELS Multifield
Dataset. We find that when models are robust to a distribution shift, they
produce substantially different representations across their layers on OOD
data. However, when they fail to generalize, these representations change less
from layer to layer on OOD data. We discuss the potential application of
similarity representation in guiding model design, training strategy, and
mitigating the OOD problem by incorporating CKA as an inductive bias during
training.

机器学习模型在处理来自未来天文调查的离域样本时依然面临挑战，解释性方法通过使用相似性度量来检查预训练卷积神经网络在中心核对齐方面的性能与表示相似性之间的关系，发现当模型对分布变化具有稳健性时，在离域数据上，图像在网络层之间的表示会有较大变化；然而，当模型无法泛化时，在离域数据上，这些表示在网络层之间的变化较小。讨论了这种相似性表示在模型设计、训练策略以及通过在训练过程中加入中心核对齐作为归纳偏差来缓解离域问题方面的潜在应用。