Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

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

doi:10.1109/access.2022.3150881

Cited by 6 publications

(4 citation statements)

References 40 publications

(70 reference statements)

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“…Besides, it is exhaustive and expensive to collect data under new operating conditions, and thus a deep metric transfer learning method in [84] is proposed to estimate the remaining useful life of bearings under different operating conditions. Some other applications of TML include galaxy morphology characterisation [85], EEG (electroencephalogram) emotion recognition [86] and drug discovery [87], and a potential application is drug-target interaction prediction [88].…”

Section: Other Applicationsmentioning

confidence: 99%

Transfer metric learning: algorithms, applications and outlooks

Luo,

Wen,

et al. 2024

Vicinagearth

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Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.

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Section: Other Applicationsmentioning

confidence: 99%

Transfer metric learning: algorithms, applications and outlooks

Luo,

Wen,

et al. 2024

Vicinagearth

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“…Specifically, the Galaxy Morphology Network (GAMORNET), a convolutional neural network (CNN; Lecun et al 1998;Krizhevsky et al 2017), classifies galaxies as either diskdominated, bulge-dominated, or indeterminate, with a precision of 99.7% (94.8%) for disk(bulge)-dominated Sloan Digital Sky Survey (SDSS; York et al 2000) galaxies and 91.8% (78.6%) for disk(bulge)-dominated CANDELS (Koekemoer et al 2011) galaxies (Ghosh et al 2020). GAMORNET can be easily trained on a large amount of simulated data first and then on a small amount of real data via transfer learning (e.g., Ackermann et al 2018;Domínguez Sánchez et al 2019;Variawa et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Tian

Urry

Ghosh

et al. 2023

ApJ

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We present a machine-learning framework to accurately characterize the morphologies of active galactic nucleus (AGN) host galaxies within z < 1. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low (0 < z < 0.25), mid (0.25 < z < 0.5), and high (0.5 < z < 1.0). By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for ∼60%–70% of the host galaxies from test sets, with a classification precision of ∼80%–95%, depending on the redshift bin. Specifically, our models achieve a disk precision of 96%/82%/79% and bulge precision of 90%/90%/80% (for the three redshift bins) at thresholds corresponding to indeterminate fractions of 30%/43%/42%. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging surveys.

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“…One reason for the success of deep learning models is their ability to transfer previous learning to new tasks. In image classification, this transfer leads to more robust models and faster training [8][9][10][11][12][13]. Despite the importance of transfer in deep learning, there has been little insight into the nature of transferring relational knowledge-that is, the representations learnt by graph neural networks.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Investigating Transfer Learning in Graph Neural Networks

2022

Self Cite

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Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems, resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further, we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

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Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

Cited by 6 publications

References 40 publications

Transfer metric learning: algorithms, applications and outlooks

Transfer metric learning: algorithms, applications and outlooks

Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

Investigating Transfer Learning in Graph Neural Networks

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