  The artistic style of a painting is a rich descriptor that reveals both
visual and deep intrinsic knowledge about how an artist uniquely portrays and
expresses their creative vision. Accurate categorization of paintings across
different artistic movements and styles is critical for large-scale indexing of
art databases. However, the automatic extraction and recognition of these
highly dense artistic features has received little to no attention in the field
of computer vision research. In this paper, we investigate the use of deep
self-supervised learning methods to solve the problem of recognizing complex
artistic styles with high intra-class and low inter-class variation. Further,
we outperform existing approaches by almost 20% on a highly class imbalanced
WikiArt dataset with 27 art categories. To achieve this, we train the EnAET
semi-supervised learning model (Wang et al., 2019) with limited annotated data
samples and supplement it with self-supervised representations learned from an
ensemble of spatial and non-spatial transformations.
