  Transient noise appearing in the data from gravitational-wave detectors
frequently causes problems, such as instability of the detectors and
overlapping or mimicking gravitational-wave signals. Because transient noise is
considered to be associated with the environment and instrument, its
classification would help to understand its origin and improve the detector's
performance. In a previous study, an architecture for classifying transient
noise using a time-frequency 2D image (spectrogram) is proposed, which uses
unsupervised deep learning combined with variational autoencoder and invariant
information clustering. The proposed unsupervised-learning architecture is
applied to the Gravity Spy dataset, which consists of Advanced Laser
Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises
with their associated metadata to discuss the potential for online or offline
data analysis. In this study, focused on the Gravity Spy dataset, the training
process of unsupervised-learning architecture of the previous study is examined
and reported.
