Continual Learning (CL) is the research field addressing learning without
forgetting when the data distribution is not static. This paper studies
spurious features' influence on continual learning algorithms. We show that
continual learning algorithms solve tasks by selecting features that are not
generalizable. Our experiments highlight that continual learning algorithms
face two related problems: (1) spurious features and (2) local spurious
features. The first one is due to a covariate shift between training and
testing data, while the second is due to the limited access to data at each
training step. We study (1) through a consistent set of continual learning
experiments varying spurious correlation amount and data distribution support.
We show that (2) is a major cause of performance decrease in continual learning
along with catastrophic forgetting. This paper presents a different way of
understanding performance decrease in continual learning by highlighting the
influence of (local) spurious features in algorithms capabilities.

本研究探讨了虚假特征对于连续学习算法的影响，发现连续学习算法通过选择无法概括的特征来解决任务。实验表明，连续学习算法面临两个相关问题：（1）虚假特征和（2）局部虚假特征。本文提供了一种不同的理解连续学习性能下降的方式，强调了（局部）虚假特征在算法性能中的影响。

持续特征选择：在持续学习中排除虚假特征

Continual Feature Selection: Spurious Features in Continual Learning

Visual modifications to text are often used to obfuscate offensive comments
in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"),
among other scenarios. We consider this as a new type of adversarial attack in
NLP, a setting to which humans are very robust, as our experiments with both
simple and more difficult visual input perturbations demonstrate. We then
investigate the impact of visual adversarial attacks on current NLP systems on
character-, word-, and sentence-level tasks, showing that both neural and
non-neural models are, in contrast to humans, extremely sensitive to such
attacks, suffering performance decreases of up to 82\%. We then explore three
shielding methods---visual character embeddings, adversarial training, and
rule-based recovery---which substantially improve the robustness of the models.
However, the shielding methods still fall behind performances achieved in
non-attack scenarios, which demonstrates the difficulty of dealing with visual
attacks.

本篇研究探讨了将文本进行视觉篡改后对 NLP 系统的攻击方法，在字符级别、单词级别和句子级别的任务中，神经模型和非神经模型的性能都会降低高达 82%，并研究了三种防御方法 —— 视觉字符嵌入、对抗性训练、基于规则的恢复 —— 这些方法可显著提高模型的鲁棒性，但与攻击未发生时的性能相比仍有一定差距。