The talk will be focused on the problem of (image) classification under a setting in which instances of previously unseen classes might appear during model usage. The model should reject those examples and ideally has the ability to add groups of them as new classes for later classification without re-training or significant model adjustments. The problem is motivated by the application we are currently working on in a project.