Deep Space Navigation With Deep Learning

Well, that’s not exactly the title but the paper does describe a better than 99% accuracy when compared to human recognition of galaxy images by type. I assume galaxy type is going to be a question on deep space navigation exams in the distant future. 😉

Rotation-invariant convolutional neural networks for galaxy morphology prediction by Sander Dieleman, Kyle W. Willett, Joni Dambre.

Abstract:

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 trained experts, which is time-consuming and does not scale to large (≳104) numbers of images.

Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.

We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.

For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (>99%) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts’ workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the LSST.

I particularly like the line:

Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation.

It reminds me of a suggestion I made for doing something quite similar where the uncertainly of crowd classifiers on a particular letter (as in a manuscript) would trigger the forwarding of that portion to an expert for a “definitive” read. You would surprised at the resistance you can encounter to the suggestion that no special skills are needed to read Greek manuscripts, which are in many cases as clear as when they were written in the early Christian era. Some aren’t and some aspects of them require expertise, but that isn’t to say they all require expertise.

Of course, if successful, such a venture could quite possibly result in papers that cite the images of all extant biblical witnesses and all of the variant texts, as opposed to those that cite a fragment entrusted to them for publication. The difference being whether you want to engage in scholarship, the act of interpreting witnesses or whether you wish to tell the proper time and make a modest noise while doing so.

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