Identifying Out-of-distribution (OOD) data is becoming increasingly critical as the applications of deep learning methods in real-world expands. The primary challenge is to ensure the model does not make incorrect predictions on OOD data with high confidence. To address this, most recent methods leverage the intermediate activations to identify distinctive signature patterns between in-distribution and OOD data. Moreover, some methods make a normality assumptions in these activations and the corresponding OOD detection algorithms are constructed based on these assumptions. However, as the model grows in size and accuracy, iterating through all intermediate layers with normality assumption may not be accurate. To tackle this, we propose a novel flow-based contrastive learning algorithm that relies primarily on the penultimate feature layer. The key component of our method is transforming the features to conform to normal distribution while retaining the class-specific information. This allows for a more robust representation learning which can be utilized for OOD detection. Our method does not require any additional data or retraining the original model, which makes it suitable for real-world applications. We emperically demonstrate the competitive performance of our method on various benchmark dataset.