This paper introduces CORAL, a simple and effective method for unsupervised domain adaptation. Unlike human learning, machine learning often struggles with changes in input distributions between training and test data, leading to poor performance. CORAL aligns the second-order statistics of source and target distributions without requiring target labels. It works by re-coloring whitened source features with the covariance of the target distribution. CORAL is simple to implement, requiring only four lines of MATLAB code, and performs well on standard benchmark datasets. It outperforms many existing methods, including recent deep CNN adaptation approaches, in tasks such as object recognition and sentiment prediction. CORAL is particularly effective with deep features, achieving better or comparable performance to more complex methods. It can be easily integrated into deep neural networks and is efficient, making it suitable for rapidly changing target domains. The method is also applicable to various feature types, including deep features, and has shown strong performance in both computer vision and natural language processing tasks. The paper also discusses the relationship between CORAL and existing methods, showing that it is a more general and simpler approach than many existing techniques. Experiments on object recognition and sentiment analysis demonstrate that CORAL achieves state-of-the-art results, outperforming many existing methods in accuracy and efficiency.This paper introduces CORAL, a simple and effective method for unsupervised domain adaptation. Unlike human learning, machine learning often struggles with changes in input distributions between training and test data, leading to poor performance. CORAL aligns the second-order statistics of source and target distributions without requiring target labels. It works by re-coloring whitened source features with the covariance of the target distribution. CORAL is simple to implement, requiring only four lines of MATLAB code, and performs well on standard benchmark datasets. It outperforms many existing methods, including recent deep CNN adaptation approaches, in tasks such as object recognition and sentiment prediction. CORAL is particularly effective with deep features, achieving better or comparable performance to more complex methods. It can be easily integrated into deep neural networks and is efficient, making it suitable for rapidly changing target domains. The method is also applicable to various feature types, including deep features, and has shown strong performance in both computer vision and natural language processing tasks. The paper also discusses the relationship between CORAL and existing methods, showing that it is a more general and simpler approach than many existing techniques. Experiments on object recognition and sentiment analysis demonstrate that CORAL achieves state-of-the-art results, outperforming many existing methods in accuracy and efficiency.