3 research outputs found
A Deep Generative Model for Interactive Data Annotation through Direct Manipulation in Latent Space
The impact of machine learning (ML) in many fields of application is
constrained by lack of annotated data. Among existing tools for ML-assisted
data annotation, one little explored tool type relies on an analogy between the
coordinates of a graphical user interface and the latent space of a neural
network for interaction through direct manipulation. In the present work, we 1)
expand the paradigm by proposing two new analogies: time and force as
reflecting iterations and gradients of network training; 2) propose a network
model for learning a compact graphical representation of the data that takes
into account both its internal structure and user provided annotations; and 3)
investigate the impact of model hyperparameters on the learned graphical
representations of the data, identifying candidate model variants for a future
user study
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
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