Publikation
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Hannes Kath; Bengt Lüers; Thiago Gouvea; Daniel Sonntag
DFKI, DFKI Research Reports (RR), Vol. 2305.15353v1, 5/2023.
Zusammenfassung
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.