Advances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection because they do not require any labels for training. The anomaly detection performance of these methods, however, varies a lot, due to the change of the intra-class variance and the difference in complexity of input samples. In addition, most previous state-of-the-art works on anomaly detection have empirically adjusted several hyperparameters to heighten their performance of anomaly detection. These sorts of procedures are known to be impractical and create obstacles in real world anomaly detection. To solve these problems, we propose a hybrid discriminator with a correlative autoencoder for anomaly detection. In the proposed framework, the discriminator implicitly estimates the conditional probability density function and the autoencoder has improved ability to control the reconstruction error. We provide theoretical foundation of our method and verify it through various experiments. We also confirm practical benefits of our interpretation of the conditional expectation and the proposed framework by comparing our results with other state-of-the-art methods.