2 research outputs found
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM
In this paper, we present a new idea for Transfer Learning (TL) based on
Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to
transfer to a new state with a higher possibility with respect to a probability
distribution. We find that such an algorithm can be employed to transfer
instances between domains. Restricted Boltzmann Machine (RBM) is an energy
based model that is very feasible for being trained to represent a data
distribution and also for performing Gibbs sampling. We used RBM to capture
data distribution of the source domain and use it in order to cast target
instances into new data with a distribution similar to the distribution of
source data. Using datasets that are commonly used for evaluation of TL
methods, we show that our method can successfully enhance target classification
by a considerable ratio. Additionally, the proposed method has the advantage
over common DA methods that it needs no target data during the process of
training of models
Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting
As a new way of human-computer interaction, inertial sensor based in-air
handwriting can provide a natural and unconstrained interaction to express more
complex and richer information in 3D space. However, most of the existing
in-air handwriting work is mainly focused on handwritten character recognition,
which makes these work suffer from poor readability of inertial signal and lack
of labeled samples. To address these two problems, we use unsupervised domain
adaptation method to reconstruct the trajectory of inertial signal and generate
inertial samples using online handwritten trajectories. In this paper, we
propose an AirWriting Translater model to learn the bi-directional translation
between trajectory domain and inertial domain in the absence of paired inertial
and trajectory samples. Through semantic-level adversarial training and latent
classification loss, the proposed model learns to extract domain-invariant
content between inertial signal and trajectory, while preserving semantic
consistency during the translation across the two domains. We carefully design
the architecture, so that the proposed framework can accept inputs of arbitrary
length and translate between different sampling rates. We also conduct
experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT
(handwritten trajectory dataset), the results on the two datasets demonstrate
that the proposed network successes in both Inertia-to Trajectory and
Trajectory-to-Inertia translation tasks