Reliable confidence estimation for deep neural classifiers is a challenging
yet fundamental requirement in high-stakes applications. Unfortunately, modern
deep neural networks are often overconfident for their erroneous predictions.
In this work, we exploit the easily available outlier samples, i.e., unlabeled
samples coming from non-target classes, for helping detect misclassification
errors. Particularly, we find that the well-known Outlier Exposure, which is
powerful in detecting out-of-distribution (OOD) samples from unknown classes,
does not provide any gain in identifying misclassification errors. Based on
these observations, we propose a novel method called OpenMix, which
incorporates open-world knowledge by learning to reject uncertain
pseudo-samples generated via outlier transformation. OpenMix significantly
improves confidence reliability under various scenarios, establishing a strong
and unified framework for detecting both misclassified samples from known
classes and OOD samples from unknown classes. The code is publicly available at
this https URL.

本研究提出了一种可靠的置信度估计方法以检测深度神经网络分类器的误分类，该方法通过利用异常样本、使用在非目标类中的无标签样本以协助检测误分类错误，并将开放世界知识结合到模型中，学习拒绝通过异常转换生成的不确定的伪样本，从而在各种场景下显著提高置信度可靠性。

OpenMix：探索异常样本进行误分类检测

OpenMix: Exploring Outlier Samples for Misclassification Detection

Daily images may convey abstract meanings that require us to memorize and
infer profound information from them. To encourage such human-like reasoning,
in this work, we teach machines to predict where and when it was taken rather
than performing basic tasks like traditional segmentation or classification.
Inspired by Horn's QR theory, we designed a novel QR-CLIP model consisting of
two components: 1) the Quantity module first retrospects more open-world
knowledge as the candidate language inputs; 2) the Relevance module carefully
estimates vision and language cues and infers the location and time.
Experiments show our QR-CLIP's effectiveness, and it outperforms the previous
SOTA on each task by an average of about 10% and 130% relative lift in terms of
location and time reasoning. This study lays a technical foundation for
location and time reasoning and suggests that effectively introducing
open-world knowledge is one of the panaceas for the tasks.

该研究设计了一种基于 Horn 的 QR 理论的 QR-CLIP 模型，教导机器在图片中预测拍摄的位置和时间，相对于传统的图像分类任务有 10% 和 130% 的提升，对于机器学习和应用开放世界知识提出了重要启示。

QR-CLIP: 为位置和时间推理引入显式的开放世界知识

QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning

Generating natural and informative texts has been a long-standing problem in
NLP. Much effort has been dedicated into incorporating pre-trained language
models (PLMs) with various open-world knowledge, such as knowledge graphs or
wiki pages. However, their ability to access and manipulate the task-specific
knowledge is still limited on downstream tasks, as this type of knowledge is
usually not well covered in PLMs and is hard to acquire. To address the
problem, we propose augmenting TExt Generation via Task-specific and Open-world
Knowledge (TegTok) in a unified framework. Our model selects knowledge entries
from two types of knowledge sources through dense retrieval and then injects
them into the input encoding and output decoding stages respectively on the
basis of PLMs. With the help of these two types of knowledge, our model can
learn what and how to generate. Experiments on two text generation tasks of
dialogue generation and question generation, and on two datasets show that our
method achieves better performance than various baseline models.

通过引入任务特定和开放世界知识（TegTok）进一步改善文本生成的性能，该方法可以在 PLMs 上学习如何更好地生成对话和问题。