Detecting out-of-distribution (OOD) instances is crucial for the reliable
deployment of machine learning models in real-world scenarios. OOD inputs are
commonly expected to cause a more uncertain prediction in the primary task;
however, there are OOD cases for which the model returns a highly confident
prediction. This phenomenon, denoted as "overconfidence", presents a challenge
to OOD detection. Specifically, theoretical evidence indicates that
overconfidence is an intrinsic property of certain neural network
architectures, leading to poor OOD detection. In this work, we address this
issue by measuring extreme activation values in the penultimate layer of neural
networks and then leverage this proxy of overconfidence to improve on several
OOD detection baselines. We test our method on a wide array of experiments
spanning synthetic data and real-world data, tabular and image datasets,
multiple architectures such as ResNet and Transformer, different training loss
functions, and include the scenarios examined in previous theoretical work.
Compared to the baselines, our method often grants substantial improvements,
with double-digit increases in OOD detection AUC, and it does not damage
performance in any scenario.

通过测量神经网络次最后一层的极端激活值并利用这一特征改进各种 OOD 检测基线，我们解决了模型 “过度自信” 现象造成的 OOD 检测问题，在各类实验中得到了显著提升，不损害任何场景的性能。

通过捕捉极端激活来减轻对于外部分布检测中的过度自信

Mitigating Overconfidence in Out-of-Distribution Detection by Capturing  Extreme Activations

When deployed for risk-sensitive tasks, deep neural networks must be able to
detect instances with labels from outside the distribution for which they were
trained. In this paper we present a novel framework to benchmark the ability of
image classifiers to detect class-out-of-distribution instances (i.e.,
instances whose true labels do not appear in the training distribution) at
various levels of detection difficulty. We apply this technique to ImageNet,
and benchmark 525 pretrained, publicly available, ImageNet-1k classifiers. The
code for generating a benchmark for any ImageNet-1k classifier, along with the
benchmarks prepared for the above-mentioned 525 models is available at
this https URL
The usefulness of the proposed framework and its advantage over alternative
existing benchmarks is demonstrated by analyzing the results obtained for these
models, which reveals numerous novel observations including: (1) knowledge
distillation consistently improves class-out-of-distribution (C-OOD) detection
performance; (2) a subset of ViTs performs better C-OOD detection than any
other model; (3) the language--vision CLIP model achieves good zero-shot
detection performance, with its best instance outperforming 96% of all other
models evaluated; (4) accuracy and in-distribution ranking are positively
correlated to C-OOD detection; and (5) we compare various confidence functions
for C-OOD detection. Our companion paper, also published in ICLR 2023 (What Can
We Learn From The Selective Prediction And Uncertainty Estimation Performance
Of 523 Imagenet Classifiers), examines the uncertainty estimation performance
(ranking, calibration, and selective prediction performance) of these
classifiers in an in-distribution setting.

本文提出了一种用于评估图像分类器检测类别 - 分布外实例能力的新框架，并将该技术应用于 ImageNet 数据集，分析结果揭示了多个新观察，包括知识蒸馏能够一致提高分布外实例检测性能，ViTs 的子集比任何其他模型都能更好地检测出分布外实例等。

基于框架的分类外分布检测基准及其在 ImageNet 上的应用

A framework for benchmarking class-out-of-distribution detection and its  application to ImageNet

In supervised machine learning, the assumption that training data is labelled
correctly is not always satisfied. In this paper, we investigate an instance of
labelling error for classification tasks in which the dataset is corrupted with
out-of-distribution (OOD) instances: data that does not belong to any of the
target classes, but is labelled as such. We show that detecting and relabelling
certain OOD instances, rather than discarding them, can have a positive effect
on learning. The proposed method uses an auxiliary classifier, trained on data
that is known to be in-distribution, for detection and relabelling. The amount
of data required for this is shown to be small. Experiments are carried out on
the FSDnoisy18k audio dataset, where OOD instances are very prevalent. The
proposed method is shown to improve the performance of convolutional neural
networks by a significant margin. Comparisons with other noise-robust
techniques are similarly encouraging.

本文探讨了监督式机器学习中标注错误的一种情况，即数据集受到了分布外的污染，我们提出的方法使用辅助分类器来检测和重新标注这些污染数据，并在 FSDnoisy18k 音频数据集上实验，结果表明该方法显著提高了卷积神经网络的性能，与其他噪声抗干扰技术的比较也是鼓舞人心的。