Due to the complex features of multi-view data, multi-view outlier detection has become a very challenging research topic in outlier detection. There are three types of outliers in multi-view data, namely class outliers, attribute outliers, and class-attribute outliers. Most of the early multi-view outlier detection methods are based on the assumption of clustering, which makes it difficult to detect outliers when there is no clustering structure in the data. In recent years, many multi-view outlier detection methods use the multi-view consistent nearest neighbor assumption instead of the clustering assumption, but they still suffer from the problem of inefficient detection of new data. In addition, most existing multi-view outlier detection methods are unsupervised, which are affected by outliers during model learning and do not work well when dealing with datasets with high outlier rates. To address these issues, this study proposes an intra-view reconstruction and cross-view generation network for effective multi-view outlier detection to detect the three types of outliers, which consists of two modules: intra-view reconstruction and cross-view generation. By training with normal data, the proposed method can fully capture the features of each view in the normal data and reconstruct and generate the corresponding views better. In addition, a new outlier calculation method is proposed to calculate the corresponding outlier scores for each sample to efficiently detect new data. Extensive experimental results show that the proposed method significantly outperforms existing methods. This is a piece of work to apply a deep model based on generative adversarial networks to multi-view outlier detection.