A Deep Generative Model for Interactive Data Annotation through Direct
  Manipulation in Latent Space
Hannes Kath Thiago S. Gouvêa Daniel Sonntag
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.