A rules-based and Transfer Learning approach for deriving the Hubble type of a galaxy from the Galaxy Zoo data

Variawa, Mohamed Z.; Zyl, Terence L. van; Woolway, Matthew

doi:10.23919/fusion45008.2020.9190462

Cited by 9 publications

(3 citation statements)

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“…Variawa et al (2020) [30] trained a ResNet50 model to predict the 37-class vectors in the Galaxy Zoo 1 dataset, achieving a RMSE of 0.0942 on the unseen test set. A decision tree, available in the original Galaxy Zoo 1 publication [3] was used to define a set of rules mapping the 37-class response vectors to Hubble types.…”

Section: A the Galaxy Zoo Projectmentioning

confidence: 99%

“…Transfer learning was employed wherein the model trained to predict the 37-class response vectors served as initialisation to train a model using the RSA catalogue to predict the Hubble labels. Variawa et al (2020) [30] reported that neither rulesbased nor transfer learning approaches could reliably predict the Hubble types of galaxies in the RSA catalogue [30].…”

Section: A the Galaxy Zoo Projectmentioning

confidence: 99%

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Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

2022

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Galaxy morphology characterisation is an important area of study, as the type and formation of galaxies offer insights into the origin and evolution of the universe. Owing to the increased availability of images of galaxies, scientists have turned to crowd-sourcing to automate the process of instance labelling. However, research has shown that using crowd-sourced labels for galaxy classification comes with many pitfalls. An alternative approach to galaxy classification is metric learning. Metric learning allows for improved representations for classification, anomaly detection, information retrieval, clustering and dimensionality reduction. Understanding the implications of this approach regarding crowd-sourced labels is of paramount importance if scientists intend to continue using them. This paper compares metric learning and classification models trained or fine-tuned on both the crowd-sourced Galaxy Zoo 2 (GZ2-H) dataset and expertly labelled EFIGI catalogue. The study uses the Revised Shapley-Ames (RSA) catalogue of bright galaxies, also labelled by experts, as an unseen test set. The RSA catalogue allows for an accurate comparison of the performance of the models at predicting the Hubble types of galaxies. The classification accuracy for the crowd-sourced and expert models indicated that the models are comparable on the surface. However, using alternative metrics, the results show that the models trained on the expert dataset outperformed the model trained on the crowd-sourced data in terms of actual vs predicted labels. Further, the results show that fine-tuning a model pre-trained on crowd-sourced data can outperform the state-of-the-art in galaxy characterisation. The models trained to predict the Hubble types of galaxies are better when fine-tuned using the Proxy-NCA and Normalised-Softmax loss functions than with other pairwise losses. The Normalised-Softmax loss yielded the best overall 9-class models with accuracies at 30.88% (GZ2-H) and 30.05% (EFIGI) and MAP values of 0.3483 (GZ2-H) and 0.3889. The Proxy-NCA loss produced the second-best overall 9-class models with accuracies at 30.33% (GZ2-H) and 20.03% (EFIGI) and MAP values of 0.3577 (GZ2-H) and 0.3917 (EFIGI). Finally, the paper highlights the need for caution when utilising crowd-sourced labels; however, it argues that transfer learning from crowd-sourced labelled data to expert-labelled data can still lead to significant improvements.

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Section: A the Galaxy Zoo Projectmentioning

confidence: 99%

Section: A the Galaxy Zoo Projectmentioning

confidence: 99%

Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

2022

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“…Transfer learning [13,14,15,16,17] allows for knowledge derived from tasks rich in data to be applied to tasks, languages, or domains where data is limited. It consist of two steps, pretraining on one task or domain (source) and domain adaptation where the learned representations are used in a different task, domain or language (target).…”

Section: Introductionmentioning

confidence: 99%

Improving Cause-of-Death Classification from Verbal Autopsy Reports

Manaka¹,

Zyl²,

Kar³

2022

Preprint

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In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a VA report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD.

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