Data coupled with the right algorithms offers the potential to save lives, protect the environment and increase profitability in different applications and domains. This potential, however, can be severely inhibited by adverse data properties specifically resulting in poor model performance, failed projects, and potentially serious social implications. This workshop will examine representation learning in the context of limited and sparse training samples, class imbalance, long-tailed distributions, rare cases and classes, and outliers. Speakers and participants will discuss the challenges and risks associated with designing, developing and learning deep representations from data with adverse properties. In addition, the workshop aims to connect researchers devoted to these topics in the traditional shallow representation learning research community and the more recent deep learning community, in order to advance novel and holistic solutions. Critically, given the growth in the application of AI to real-world decision making, the workshop will also facilitate a discussion of the potential social issues associated with application of deep representation learning in the context of data adversity. The workshop will bring together theoretical and applied deep learning researchers from academia and industry, and lay the groundwork for fruitful research collaborations that span communities that are often siloed.