47,583 research outputs found
Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse
LSST and Euclid must address the daunting challenge of analyzing the
unprecedented volumes of imaging and spectroscopic data that these
next-generation instruments will generate. A promising approach to overcoming
this challenge involves rapid, automatic image processing using appropriately
trained Deep Learning (DL) algorithms. However, reliable application of DL
requires large, accurately labeled samples of training data. Galaxy Zoo Express
(GZX) is a recent experiment that simulated using Bayesian inference to
dynamically aggregate binary responses provided by citizen scientists via the
Zooniverse crowd-sourcing platform in real time. The GZX approach enables
collaboration between human and machine classifiers and provides rapidly
generated, reliably labeled datasets, thereby enabling online training of
accurate machine classifiers. We present selected results from GZX and show how
the Bayesian aggregation engine it uses can be extended to efficiently provide
object-localization and bounding-box annotations of two-dimensional data with
quantified reliability. DL algorithms that are trained using these annotations
will facilitate numerous panchromatic data modeling tasks including
morphological classification and substructure detection in direct imaging, as
well as decontamination and emission line identification for slitless
spectroscopy. Effectively combining the speed of modern computational analyses
with the human capacity to extrapolate from few examples will be critical if
the potential of forthcoming large-scale surveys is to be realized.Comment: 5 pages, 1 figure. To appear in Proceedings of the International
Astronomical Unio
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label uncertainty can be extracted from a state-of-the-art
object detection system via Dropout Sampling. We evaluate this approach on a
large synthetic dataset of 30,000 images, and a real-world dataset captured by
a mobile robot in a versatile campus environment. We show that this uncertainty
can be utilized to increase object detection performance under the open-set
conditions that are typically encountered in robotic vision. A Dropout Sampling
network is shown to achieve a 12.3% increase in recall (for the same precision
score as a standard network) and a 15.1% increase in precision (for the same
recall score as the standard network).Comment: to appear in IEEE International Conference on Robotics and Automation
2018 (ICRA 2018
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