DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

Kavli Affiliate: Brian Nord

| First 5 Authors: Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder, F. Javier Sánchez, Gabriel Nathan Perdue

| Summary:

Data processing and analysis pipelines in cosmological survey experiments
introduce data perturbations that can significantly degrade the performance of
deep learning-based models. Given the increased adoption of supervised deep
learning methods for processing and analysis of cosmological survey data, the
assessment of data perturbation effects and the development of methods that
increase model robustness are increasingly important. In the context of
morphological classification of galaxies, we study the effects of perturbations
in imaging data. In particular, we examine the consequences of using neural
networks when training on baseline data and testing on perturbed data. We
consider perturbations associated with two primary sources: 1) increased
observational noise as represented by higher levels of Poisson noise and 2)
data processing noise incurred by steps such as image compression or telescope
errors as represented by one-pixel adversarial attacks. We also test the
efficacy of domain adaptation techniques in mitigating the perturbation-driven
errors. We use classification accuracy, latent space visualizations, and latent
space distance to assess model robustness. Without domain adaptation, we find
that processing pixel-level errors easily flip the classification into an
incorrect class and that higher observational noise makes the model trained on
low-noise data unable to classify galaxy morphologies. On the other hand, we
show that training with domain adaptation improves model robustness and
mitigates the effects of these perturbations, improving the classification
accuracy by 23% on data with higher observational noise. Domain adaptation also
increases by a factor of ~2.3 the latent space distance between the baseline
and the incorrectly classified one-pixel perturbed image, making the model more
robust to inadvertent perturbations.

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