محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Recurso digital |
| اللغة: | الإنجليزية |
| منشور في: |
Zenodo
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.5281/zenodo.3685516 |
| الوسوم: |
إضافة وسم
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جدول المحتويات:
- <p>We present an analysis of the impact of neural-based domain adaptation in astronomical spectroscopy. Domain adaptation addresses the problem of applying prior knowledge to a new data of interest. Therefore, we selected a problem of quasar identification in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope survey using labelled data from the Sloan Digital Sky Survey. We choose to experiment with four neural models for domain adaptation: Deep Domain Confusion, Deep Correlation Alignment, Domain-Adversarial Network and Deep Reconstruction-Classification Network. However, our experiments reveal that these model cannot improve classification performance in comparison to a convolutional neural network that does not consider domain adaptation. Using dimensionality reduction, statistics of the selected methods and misclassifications, we show that the domain adaptation methods are not robust enough to be applied to the complex and dirty astronomical data.</p>