Rotation-invariant convolutional neural networks for galaxy morphology prediction
نویسندگان
چکیده
منابع مشابه
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by ...
متن کاملScale-Invariant Convolutional Neural Networks
Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited. The popular practise is making the model bigger first, and then train it with data augmentation using extensive scale-jittering. In this paper, we propose a scaleinvariant convolutional neural network (SiCNN), a model designed...
متن کاملDeep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using rando...
متن کاملLocally Scale-Invariant Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks. With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales. Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain ...
متن کاملSignal Correlation Prediction Using Convolutional Neural Networks
This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics contest dataset of fluorescence imaging of neural activity recordings – the aim is reconstruction of the wiring between brain neurons. The model is implemented to achieve high evaluation score. Our model took the fourth place in this contest. The perf...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Monthly Notices of the Royal Astronomical Society
سال: 2015
ISSN: 1365-2966,0035-8711
DOI: 10.1093/mnras/stv632