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Found 10 papers, 5 papers with code
Scaling Laws for Galaxy Images
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science
Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers.
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)
Development of robust general purpose 3D segmentation frameworks using the latest deep learning techniques is one of the active topics in various bio-medical domains.
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model
We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts.
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research
Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade.
Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.
An old stellar population or diffuse nebular continuum emission discovered in green pea galaxies
We use new HST images of nine Green Pea Galaxies (GPGs) to study their resolved structure and color.
Astrophysics of Galaxies
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.
Machine Learning for the Zwicky Transient Facility
The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night.
Instrumentation and Methods for Astrophysics
