11 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Morphology classification
Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.
The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance.
In this work, we studied the performance of Capsule Network, a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification.
To reduce the required computational costs, we apply machine learning techniques for clustering and consequent prediction of the simulated polymer blend images in conjunction with simulations.
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy.
Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images
To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets.
Therefore, this paper implements unsupervised learning techniques to classify the Galaxy Zoo DECaLS dataset.
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).