Exploring the Open World Using Incremental Extreme Value Machines

TL;DR

This paper presents an enhanced Extreme Value Machine for open world recognition that adapts incrementally, significantly reduces training time, and maintains high accuracy in image classification and face recognition tasks.

Contribution

It introduces a modified EVM with partial model fitting and a weighted maximum K-set cover for complexity control, improving efficiency and scalability in open world recognition.

Findings

Training time reduced by a factor of 28.

Model complexity reduced by a factor of 3.5.

Achieves about 12% higher accuracy in experiments.

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.