A Unified Approach Towards Active Learning and Out-of-Distribution Detection

Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

Keywords: Out-of-Distribution Detection; Active Learning;

Abstract:

When applying deep learning models in real-world scenarios, active learning (AL) strategies are crucial for identifying label candidates from a nearly infinite amount of unlabeled data. In this context, robust out-of-distribution (OOD) detection mechanisms are essential for handling data outside the target distribution of the application.
However, current works investigate both problems separately.
In this work, we introduce SISOM as the first unified solution for both AL and OOD detection.
By leveraging feature space distance metrics SISOM combines the strengths of the currently independent tasks to solve both effectively.
We conduct extensive experiments showing the problems arising when migrating between both tasks. In these evaluations SISOM underlined its effectiveness by achieving first place in two of the widely used OpenOOD benchmarks and second place in the remaining one. In AL, SISOM outperforms others and delivers top-1 performance in three benchmarks.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 10823