Exploring the Open World Using Incremental Extreme Value Machines

• URL: http://arxiv.org/abs/2205.14892v1
• Date: Mon, 30 May 2022 07:21:13 GMT
• Title: Exploring the Open World Using Incremental Extreme Value Machines
• Authors: Tobias Koch, Felix Liebezeit, Christian Riess, Vincent Christlein, Thomas K\"ohler
• Abstract summary: Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces a modification of the widely known Extreme Value Machine to enable open world recognition. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.
• Score: 11.3660790934494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces 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. This reduces the training time by a factor of 28. 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 by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.

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