Publication
A Deep Generative Model for Interactive Data Annotation through Direct Manipulation in Latent Space
Hannes Kath; Thiago Gouvea; Daniel Sonntag
DFKI, DFKI Research Reports (RR), Vol. 2305.15337v1, 5/2023.
Abstract
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.