Unlocking Robustness in Deep Neural Networks through Representation Similarity Analysis

In this article, we explore the relationship between how similar a neural network’s layers are and its ability to generalize to unexpected data. We use a technique called Centered Kernel Alignment (CKA) to measure the similarity of these layers in pre-trained Convolutional Neural Networks (CNNs) on a dataset called CAMELS Multifield.
Imagine you have a recipe for your favorite dish, and you want to know if it will still taste good if you change some of the ingredients. Just like how a neural network can learn to recognize different types of images, but might not work as well with completely new ones. We found that when a neural network is good at generalizing to new data, its layers are more different from each other. But when it struggles to adapt, they stay similar throughout.
We also discovered that using CKA can help identify which layers of the neural network are causing problems. It’s like having a map of your kitchen with labels on all the ingredients, and you can see where the recipe is getting mixed up. By understanding which parts of the network need improvement, we can make adjustments to make it more robust to unexpected data.
In summary, this article demonstrates how measuring the similarity of layers in a neural network can help improve its ability to generalize to new data, and how CKA can be used to identify areas that need improvement. By understanding these relationships, we can build better and more reliable machine learning models for astronomy and cosmology applications.