When evaluated in dynamic, open-world situations, neural networks struggle to
detect unseen classes. This issue complicates the deployment of continual
learners in realistic environments where agents are not explicitly informed
when novel categories are encountered. A common family of techniques for
detecting novelty relies on thresholds of similarity between observed data
points and the data used for training. However, these methods often require
manually specifying (ahead of time) the value of these thresholds, and are
therefore incapable of adapting to the nature of the data. We propose a new
method for automatically selecting these thresholds utilizing a linear search
and leave-one-out cross-validation on the ID classes. We demonstrate that this
novel method for selecting thresholds results in improved total accuracy on
MNIST, Fashion MNIST, and CIFAR-10.

在动态、开放的情境中评估时，神经网络在检测未知类别时遇到困难。本文提出了一种新方法，通过线性搜索和基于样本交叉验证选择门槛值，自动适应性地检测新颖性，从而提高 MNIST、Fashion MNIST 和 CIFAR-10 的总准确率。

阈值式新颖检测的持续改进

Continual Improvement of Threshold-Based Novelty Detection

Inspired by deep convolution segmentation algorithms, scene text detectors
break the performance ceiling of datasets steadily. However, these methods
often encounter threshold selection bottlenecks and have poor performance on
text instances with extreme aspect ratios. In this paper, we propose to
automatically learn the discriminate segmentation threshold, which
distinguishes text pixels from background pixels for segmentation-based scene
text detectors and then further reduces the time-consuming manual parameter
adjustment. Besides, we design a Global-information Enhanced Feature Pyramid
Network (GE-FPN) for capturing text instances with macro size and extreme
aspect ratios. Following the GE-FPN, we introduce a cascade optimization
structure to further refine the text instances. Finally, together with the
proposed threshold learning strategy and text detection structure, we design an
Adaptive Segmentation Network (ASNet) for scene text detection. Extensive
experiments are carried out to demonstrate that the proposed ASNet can achieve
the state-of-the-art performance on four text detection benchmarks, i.e., ICDAR
2015, MSRA-TD500, ICDAR 2017 MLT and CTW1500. The ablation experiments also
verify the effectiveness of our contributions.

基于深度卷积分割算法的场景文本检测器通过学习判别性分割阈值和设计全局信息增强特征金字塔网络，实现了对具有极端长宽比的文本实例的有效检测和自适应分割，为场景文本检测提供了最新的性能表现。