Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset. (arXiv:2208.03623v1 [gr-qc] CROSS LISTED)

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 …

architecture arxiv dataset gravity learning process training unsupervised unsupervised learning