  The detection of gravitational waves with LIGO and Virgo requires a detailed
understanding of the response of these instruments in the presence of
environmental and instrumental noise. Of particular interest is the study of
anomalous non-Gaussian noise transients known as glitches, since their high
occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational
wave signals. Therefore, successfully identifying and excising glitches is of
utmost importance to detect and characterize gravitational waves. In this
article, we present the first application of Deep Learning combined with
Transfer Learning for glitch classification, using real data from LIGO's first
discovery campaign labeled by Gravity Spy, showing that knowledge from
pre-trained models for real-world object recognition can be transferred for
classifying spectrograms of glitches. We demonstrate that this method enables
the optimal use of very deep convolutional neural networks for glitch
classification given small unbalanced training datasets, significantly reduces
the training time, and achieves state-of-the-art accuracy above 98.8%. Once
trained via transfer learning, we show that the networks can be truncated and
used as feature extractors for unsupervised clustering to automatically group
together new classes of glitches and anomalies. This novel capability is of
critical importance to identify and remove new types of glitches which will
occur as the LIGO/Virgo detectors gradually attain design sensitivity.
