  We propose to learn latent space representations of radio galaxies, and train
a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an
unlabeled dataset, to this end. We show that the encoded features can be
leveraged for downstream tasks such as classifying galaxies in labeled
datasets, and similarity search. Results show that the model is able to
reconstruct its given inputs, capturing the salient features of the latter. We
use the latent codes of galaxy images, from MiraBest Confident and FR-DEEP NVSS
datasets, to train various non-neural network classifiers. It is found that the
latter can differentiate FRI from FRII galaxies achieving \textit{accuracy}
$\ge 76\%$, \textit{roc-auc} $\ge 0.86$, \textit{specificity} $\ge 0.73$ and
\textit{recall} $\ge 0.78$ on MiraBest Confident dataset, comparable to results
obtained in previous studies. The performance of simple classifiers trained on
FR-DEEP NVSS data representations is on par with that of a deep learning
classifier (CNN based) trained on images in previous work, highlighting how
powerful the compressed information is. We successfully exploit the learned
representations to search for galaxies in a dataset that are semantically
similar to a query image belonging to a different dataset. Although generating
new galaxy images (e.g. for data augmentation) is not our primary objective, we
find that the \protect\Verb+VDVAE+ model is a relatively good emulator.
Finally, as a step toward detecting anomaly/novelty, a density estimator --
Masked Autoregressive Flow (\protect\Verb+MAF+) -- is trained on the latent
codes, such that the log-likelihood of data can be estimated. The downstream
tasks conducted in this work demonstrate the meaningfulness of the latent
codes.
