  In many applications, Neural Nets (NNs) have classification performance on
par or even exceeding human capacity. Moreover, it is likely that NNs leverage
underlying features that might differ from those humans perceive to classify.
Can we "reverse-engineer" pertinent features to enhance our scientific
understanding? Here, we apply this idea to the notoriously difficult task of
galaxy classification: NNs have reached high performance for this task, but
what does a neural net (NN) "see" when it classifies galaxies? Are there
morphological features that the human eye might overlook that could help with
the task and provide new insights? Can we visualize tracers of early evolution,
or additionally incorporated spectral data? We present a novel way to summarize
and visualize galaxy morphology through the lens of neural networks, leveraging
Dataset Distillation, a recent deep-learning methodology with the primary
objective to distill knowledge from a large dataset and condense it into a
compact synthetic dataset, such that a model trained on this synthetic dataset
achieves performance comparable to a model trained on the full dataset. We
curate a class-balanced, medium-size high-confidence version of the Galaxy Zoo
2 dataset, and proceed with dataset distillation from our accurate
NN-classifier to create synthesized prototypical images of galaxy morphological
features, demonstrating its effectiveness. Of independent interest, we
introduce a self-adaptive version of the state-of-the-art Matching Trajectory
algorithm to automate the distillation process, and show enhanced performance
on computer vision benchmarks.
