Towards out-of-distribution generalization in large-scale astronomical
  surveys: robust networks learn similar representations
Yash Gondhalekar Sultan Hassan Naomi Saphra Sambatra Andrianomena
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.