A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
This work addresses the critical issue of unknown sample detection in deep learning, providing a standardized benchmark for researchers, though it is incremental as it builds on existing tasks without introducing a new method.
The authors tackled the problem of deep neural networks making over-confident predictions on unknown samples by proposing a unified benchmark for unknown detection, integrating previous tasks like misclassification detection and out-of-distribution detection. They found that Deep Ensemble consistently outperformed other methods, but all approaches were only successful for specific types of unknowns.
Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples. Many studies have been proposed to successfully filter out these unknown samples, but they only considered narrow and specific tasks, referred to as misclassification detection, open-set recognition, or out-of-distribution detection. In this work, we argue that these tasks should be treated as fundamentally an identical problem because an ideal model should possess detection capability for all those tasks. Therefore, we introduce the unknown detection task, an integration of previous individual tasks, for a rigorous examination of the detection capability of deep neural networks on a wide spectrum of unknown samples. To this end, unified benchmark datasets on different scales were constructed and the unknown detection capabilities of existing popular methods were subject to comparison. We found that Deep Ensemble consistently outperforms the other approaches in detecting unknowns; however, all methods are only successful for a specific type of unknown. The reproducible code and benchmark datasets are available at https://github.com/daintlab/unknown-detection-benchmarks .