A Virtual Reality Tool for Representing, Visualizing and Updating Deep
  Learning Models
Hannes Kath Bengt Lüers Thiago S. Gouvêa Daniel Sonntag
Deep learning is ubiquitous, but its lack of transparency limits its impact
on several potential application areas. We demonstrate a virtual reality tool
for automating the process of assigning data inputs to different categories. A
dataset is represented as a cloud of points in virtual space. The user explores
the cloud through movement and uses hand gestures to categorise portions of the
cloud. This triggers gradual movements in the cloud: points of the same
category are attracted to each other, different groups are pushed apart, while
points are globally distributed in a way that utilises the entire space. The
space, time, and forces observed in virtual reality can be mapped to
well-defined machine learning concepts, namely the latent space, the training
epochs and the backpropagation. Our tool illustrates how the inner workings of
deep neural networks can be made tangible and transparent. We expect this
approach to accelerate the autonomous development of deep learning applications
by end users in novel areas.