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

Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

Abstract:

In real-world applications of deep learning models, active learning (AL) strategies are essential for identifying label candidates from vast amounts of unlabeled data. In this context, robust out-of-distribution (OOD) detection mechanisms are crucial for handling data out-
side the target distribution during the application’s operation. Usually, these problems have been addressed separately. In this work, we introduce SISOM as a unified solution designed explicitly for AL and OOD detection. By combining feature space-based and uncertainty-
based metrics, SISOM leverages the strengths of the currently independent tasks to solve both effectively, without requiring specific training schemes. We conducted extensive experiments showing the problems arising when migrating between both tasks. In our experiments SISOM underlined its effectiveness by achieving first place in one of the commonly used OpenOOD benchmark settings and top-3 places in the remaining two for near-OOD data. In AL, SISOM delivers top performance in common image benchmarks.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/TUM-DAML/SISOM

Assigned Action Editor: ~Chicheng_Zhang1

Submission Number: 4629