When applying deep learning models in open-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

SISOM is a unified solution that combines the strengths of active learning and out-of-distribution detection, achieving top performance in benchmark evaluations.

一个统一的方法用于主动学习和外域检测

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

Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than
learning the intended task, they tend to draw inconclusive relationships
between their inputs and outputs. Shortcut learning is ubiquitous among many
failure cases of neural networks, and traces of this phenomenon can be seen in
their generalizability issues, domain shift, adversarial vulnerability, and
even bias towards majority groups. In this paper, we argue that this
commonality in the cause of various DNN issues creates a significant
opportunity that should be leveraged to find a unified solution for shortcut
learning. To this end, we outline the recent advances in topological data
analysis~(TDA), and persistent homology~(PH) in particular, to sketch a unified
roadmap for detecting shortcuts in deep learning. We demonstrate our arguments
by investigating the topological features of computational graphs in DNNs using
two cases of unlearnable examples and bias in decision-making as our test
studies. Our analysis of these two failure cases of DNNs reveals that finding a
unified solution for shortcut learning in DNNs is not out of reach, and TDA can
play a significant role in forming such a framework.

深度神经网络易受到捷径学习的影响，本文通过拓扑数据分析和持久同调的研究，找出一种能够解决深度学习中的捷径学习问题的统一方法。