15 research outputs found
Emerging Convolutions for Generative Normalizing Flows
Generative flows are attractive because they admit exact likelihood
optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018)
demonstrated with Glow that generative flows are capable of generating high
quality images. We generalize the 1 x 1 convolutions proposed in Glow to
invertible d x d convolutions, which are more flexible since they operate on
both channel and spatial axes. We propose two methods to produce invertible
convolutions that have receptive fields identical to standard convolutions:
Emerging convolutions are obtained by chaining specific autoregressive
convolutions, and periodic convolutions are decoupled in the frequency domain.
Our experiments show that the flexibility of d x d convolutions significantly
improves the performance of generative flow models on galaxy images, CIFAR10
and ImageNet.Comment: Accepted at International Conference on Machine Learning (ICML) 201
A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to the
one established for traditional software development. A key difference between
the two lies in the strong dependency between the quality of a machine learning
model and the quality of the data used to train or perform evaluations. In this
work, we demonstrate how different aspects of data quality propagate through
various stages of machine learning development. By performing a joint analysis
of the impact of well-known data quality dimensions and the downstream machine
learning process, we show that different components of a typical MLOps pipeline
can be efficiently designed, providing both a technical and theoretical
perspective
Search for the edge-on galaxies using an artificial neural network
We present an application of an artificial neural network methodology to a
modern wide-field sky survey Pan-STARRS1 in order to build a high-quality
sample of disk galaxies visible in edge-on orientation. Such galaxies play an
important role in the study of the vertical distribution of stars, gas and
dust, which is usually not available to study in other galaxies outside the
Milky Way. We give a detailed description of the network architecture and the
learning process. The method demonstrates good effectiveness with detection
rate about 97\% and it works equally well for galaxies over a wide range of
brightnesses and sizes, which resulted in a creation of a catalogue of edge-on
galaxies with of objects. The catalogue is published on-line with an
open access.Comment: 15 pages, 11 figure
Galaxy Image Classification Based on Citizen Science Data: A Comparative Study
Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur-and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results