Representation learning is concerned with finding semantically useful and compact, i.e., low dimensional, representations for inputs from selected data distributions. These representations can be efficiently used for downstream tasks, like classification or semantic-similarity search. Common approaches include learning representations that are invariant under augmentations (e.g. shifts and small rotations for images) or finding low-dimensional encodings of words or sentences that carry semantic meaning.