Standard closed-set deep learning approaches deployed in the real-world fail when test samples come from never-seen data distributions, which makes them untrustworthy for safety-critical applications such as autonomous driving or healthcare. A preferable behavior would be to raise an alert in case of unknown object categories, a task known as Out-Of-Distribution (OOD) detection. The common strategy to handle this task is to train (or at least fine-tune) the detector on the target data to make it learn the normality distribution, to then recognize test samples not belonging to it. Consequently, It would be necessary to gather a great amount of labeled target data and to perform the training procedure for each distinct downstream task.