I am an external PhD candidate in the Multimedia Security Group and work in cooperation with e.solutions GmbH. My research interests include classical machine and deep learning, with a focus on online learning and open-set recognition. The scope of my work covers biometrics, such as face recognition and face anti-spoofing, as well as image classification in general.
Exploring the Open World Using Incremental Extreme Value Machines (ICPR 2022)
Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open-world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. We introduce a modification of the widely known Extreme Value Machine (EVM) to enable open-world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort.
Below is the code base for the incremental EVM. Although this is not the original code, it should help to reproduce our work and develop further ideas.