Fraunhofer Chalmers Research Centre for Industrial Mathematics
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Growing Artificial Neural Networks Novel approaches to Deep Learning for Image Analysis and Particle Tracking
No full textDeep-learning has recently emerged as one of the most successful methods for analyzing
large amounts of data and constructing models from it. It has revolutionized the
field of image analysis and the algorithms are now being employed in research field
outside of computer science. The methods do however suffer from several drawbacks
such as large computational costs.
In this thesis alternative methods for training the underlying networks are evaluated.
These methods are based on gradually growing networks during training using
layer-by-layer training as well as increasing network width. These training methods
lend themselves to easily implementing networks of tune-able size allowing for tradeoff
between high accuracy and fast execution or the construction of modular networks
in which one can chose to execute only a small part of the network to get a very fast
prediction at the cost of some accuracy. The layer-by-layer method is applied to multiple
different image analysis tasks and the performance is evaluated and compared to
that of regular training. Both the layer by layer training and the breadth training are
comparable to normal training in performance. The modular nature of the networks
make them suitable for applications within multi-particle tracking
Bågbro över Viskan Förstudie innefattande konceptuell design och preliminärdimensionering av vägbro
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