For real-world language applications, detecting an out-of-distribution (OOD)
sample is helpful to alert users or reject such unreliable samples. However,
modern over-parameterized language models often produce overconfident
predictions for both in-distribution (ID) and OOD samples. In particular,
language models suffer from OOD samples with a similar semantic representation
to ID samples since these OOD samples lie near the ID manifold. A rejection
network can be trained with ID and diverse outlier samples to detect test OOD
samples, but explicitly collecting auxiliary OOD datasets brings an additional
burden for data collection. In this paper, we propose a simple but effective
method called Pseudo Outlier Exposure (POE) that constructs a surrogate OOD
dataset by sequentially masking tokens related to ID classes. The surrogate OOD
sample introduced by POE shows a similar representation to ID data, which is
most effective in training a rejection network. Our method does not require any
external OOD data and can be easily implemented within off-the-shelf
Transformers. A comprehensive comparison with state-of-the-art algorithms
demonstrates POE's competitiveness on several text classification benchmarks.

我们提出了一种名为 Pseudo Outlier Exposure（POE）的简单但有效的方法，通过顺序遮盖与 ID 类相关的令牌来构建一个替代性的 OOD 数据集，该替代性 OOD 样本与 ID 数据具有相似的表示，可以在训练拒绝网络方面发挥最有效的作用，与最先进的算法进行全面比较，在几个文本分类基准测试中证明了 POE 的竞争力。

使用预训练转换器的伪异常值暴露进行对外分布检测

Pseudo Outlier Exposure for Out-of-Distribution Detection using  Pretrained Transformers

As the use of machine learning continues to expand, the importance of
ensuring its safety cannot be overstated. A key concern in this regard is the
ability to identify whether a given sample is from the training distribution,
or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can
manipulate OOD samples in ways that lead a classifier to make a confident
prediction. In this study, we present a novel approach for certifying the
robustness of OOD detection within a $\ell_2$-norm around the input, regardless
of network architecture and without the need for specific components or
additional training. Further, we improve current techniques for detecting
adversarial attacks on OOD samples, while providing high levels of certified
and adversarial robustness on in-distribution samples. The average of all OOD
detection metrics on CIFAR10/100 shows an increase of $\sim 13 \% / 5\%$
relative to previous approaches.

研究机器学习中如何确定训练分布和 ODD（Out-Of-Distribution）样本的检测机制，提出了一种基于 l2 范数的无需特定组件或训练的 ODD 鲁棒性证明的新方法以及改进了检测 ODD 攻击技巧的现有技术，在 CIFAR10 / 100 平均 OOD 检测度量方面相对于之前的方法有约 13％/ 5％的提高，并在分布内样本上提供高水平的认证和敌对鲁棒性。