Beyond the closed-world assumption : The importance of novelty detection and open set recognition Computer Vision Group Jena

Motivation: Current work on visual object recognition focuses on object classification and is implicitly based on the closed-world assumption, i.e., a test sample is assigned to the most plausible class out of a fixed set of classes known during training. Knowledge about objects and classes is usually available in terms of representative training data and is used for model training. However, in real-world applications it is often not possible to obtain training data for all categories that can occur in the test phase beforehand. An example is quality control, where it is not only impossible to define all future defects – even worse, possible defects are in most cases not even known to the human expert who supervises the training step. In addition, even if one would know possible defects a priori, the small number of training images leads to ill-posed problems. A second application is life-long learning where a system needs to identify new, unknown objects classes and has to incrementally add them to its knowledge base. Finally, complex event detection in videos is also to impossible to tackle with a fixed set of classes. Although several solutions for the novelty detection problem have been proposed during the past years, they usually suffer from strong limitations (e.g., model complexity), necessary assumptions (e.g., Gaussian distribution), or heuristics (e.g., separation from artificial negative data). On top of that, it is unknown so far whether or not such methods can successfully be applied in an open set scenario.