Abstract: Anomaly detection (AD) is a fundamental challenge in machine learning that finds samples that do not belong to the distribution of the training data. Recently self-supervised learning approaches and, in particular, contrastive learning show promising results in various machine vision applications mitigating the hunger of traditional supervised deep learning approaches for an enormous amount of labeled data. In this work, we adopt the idea of contrastive learning for reconstruction-based anomaly detection models. Our contrastive learning approach contrasts the sample with local feature maps of itself instead of contrasting a given sample with other instances as in conventional contrastive learning approaches. Our anomaly detection model based on contrastive generative adversarial network, AD-CGAN, is shown to obtain state-of-the-art performance in multiple benchmark datasets. AD-CGAN outperforms the existing reconstruction-based approaches by more than $15\%$ ROC-AUC in several benchmark experiments.