  We investigate the use of Convolutional Neural Networks (including the modern
ConvNeXt network family) to classify transient noise signals (i.e.~glitches)
and gravitational waves in data from the Advanced LIGO detectors. First, we use
models with a supervised learning approach, both trained from scratch using the
Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained
models in this dataset. Second, we also explore a self-supervised approach,
pre-training models with automatically generated pseudo-labels. Our findings
are very close to existing results for the same dataset, reaching values for
the F1 score of 97.18% (94.15%) for the best supervised (self-supervised)
model. We further test the models using actual gravitational-wave signals from
LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and
O2), the models show good performance, in particular when using transfer
learning. We find that transfer learning improves the scores without the need
for any training on real signals apart from the less than 50 chirp examples
from hardware injections present in the Gravity Spy dataset. This motivates the
use of transfer learning not only for glitch classification but also for signal
classification.
