English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many applications, Neural Nets (NNs) have classification performance onpar or even exceeding human capacity. Moreover, it is likely that NNs leverageunderlying features that might differ from those humans perceive to classify.Can we "reverse-engineer" pertinent features to enhance our scientificunderstanding? Here, we apply this idea to the notoriously difficult task ofgalaxy classification: NNs have reached high performance for this task, butwhat does a neural net (NN) "see" when it classifies galaxies? Are theremorphological features that the human eye might overlook that could help withthe task and provide new insights? Can we visualize tracers of early evolution,or additionally incorporated spectral data? We present a novel way to summarizeand visualize galaxy morphology through the lens of neural networks, leveragingDataset Distillation, a recent deep-learning methodology with the primaryobjective to distill knowledge from a large dataset and condense it into acompact synthetic dataset, such that a model trained on this synthetic datasetachieves performance comparable to a model trained on the full dataset. Wecurate a class-balanced, medium-size high-confidence version of the Galaxy Zoo2 dataset, and proceed with dataset distillation from our accurateNN-classifier to create synthesized prototypical images of galaxy morphologicalfeatures, demonstrating its effectiveness. Of independent interest, weintroduce a self-adaptive version of the state-of-the-art Matching Trajectoryalgorithm to automate the distillation process, and show enhanced performanceon computer vision benchmarks.

Discovering Galaxy Features via Dataset Distillation