The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to \textit{distributional vulnerability} in deep neural networks, which can subsequently lead to high-confidence predictions for OOD samples. This is mainly due to the absence of OOD samples during training, which fails to constrain the network properly. To tackle this issue, several state-of-the-art methods include adding extra OOD samples to training and assign them with manually-defined labels. However, this practice can introduce unreliable labeling, negatively affecting ID classification. The distributional vulnerability presents a critical challenge for non-IID deep learning, which aims for OOD-tolerant ID classification by balancing ID generalization and OOD detection. In this paper, we introduce a novel \textit{supervision adaptation} approach to generate adaptive supervision information for OOD samples, making them more compatible with ID samples. Firstly, we measure the dependency between ID samples and their labels using mutual information, revealing that the supervision information can be represented in terms of negative probabilities across all classes. Secondly, we investigate data correlations between ID and OOD samples by solving a series of binary regression problems, with the goal of refining the supervision information for more distinctly separable ID classes. Our extensive experiments on four advanced network architectures, two ID datasets, and eleven diversified OOD datasets demonstrate the efficacy of our supervision adaptation approach in improving both ID classification and OOD detection capabilities.

翻译：域内（ID）与域外（OOD）样本之间的差异可能导致深度神经网络的**分布脆弱性**，进而使模型对OOD样本产生高置信度预测。这主要源于训练过程中缺乏OOD样本，未能对网络形成有效约束。为解决该问题，部分先进方法在训练中引入额外OOD样本并赋予人工定义标签，但此类做法可能引入不可靠标注，对ID分类产生负面影响。分布脆弱性对非独立同分布深度学习构成严峻挑战——该领域旨在通过平衡ID泛化与OOD检测实现容忍OOD样本的ID分类。本文提出一种新颖的**监督适应性**方法，为OOD样本生成自适应监督信息，使其与ID样本更具兼容性。首先，我们利用互信息度量ID样本与其标签的依赖关系，揭示监督信息可表示为各类别负概率形式；其次，通过求解一系列二元回归问题探究ID与OOD样本间的数据相关性，旨在优化监督信息以实现更清晰的ID类别分离。我们在四种先进网络架构、两个ID数据集及十一个多样化OOD数据集上的大量实验表明，所提出的监督适应性方法在提升ID分类与OOD检测能力方面均具有显著效果。