(1) Learning visual representations for unfamiliar environments is challenging due to domain shift between training and test data distributions. (2) The paper proposes learning asymmetric transformations to map target domain data to the source domain in order to address this domain shift problem. (3) The key aspects of the approach include learning nonlinear kernel-based transformations between domains in a regularized manner and evaluating its ability to generalize to novel target classes not seen during training.