Thank you so much for your interest. Deep learning has had tremendous success recently but it often makes one important assumption, that is whatever data the mo

Thank you so much for your interest. Deep learning has had tremendous success recently but it often makes one important assumption, that is whatever data the model sees during deployment should be 'similar' to what it was trained on (aka in-distribution). Unfortunately, the real world is not static and constantly evolving, and so is the data we feed into the model. Equipping the model with the ability to say no when it is not familiar with the input is very important, especially when it comes to safety-critical applications.