We propose a novel framework DropTop that suppresses the shortcut bias in
online continual learning (OCL) while being adaptive to the varying degree of
the shortcut bias incurred by continuously changing environment. By the
observed high-attention property of the shortcut bias, highly-activated
features are considered candidates for debiasing. More importantly, resolving
the limitation of the online environment where prior knowledge and auxiliary
data are not ready, two novel techniques -- feature map fusion and adaptive
intensity shifting -- enable us to automatically determine the appropriate
level and proportion of the candidate shortcut features to be dropped.
Extensive experiments on five benchmark datasets demonstrate that, when
combined with various OCL algorithms, DropTop increases the average accuracy by
up to 10.4% and decreases the forgetting by up to 63.2%.

DropTop 提出了一种新颖的框架，可以抑制在线持续学习中的快捷方式偏差，并能够适应不断变化的环境所引起的不同程度的快捷方式偏差。通过观察到的快捷方式偏差的高关注特性，高度激活的特征被视为去偏差的候选项。此外，通过解决在线环境中缺乏先前的知识和辅助数据的限制，两种新技术 —— 特征图融合和自适应强度调整 —— 使我们能够自动确定适当的级别和候选快捷方式特征的比例要丢弃。在五个基准数据集上进行的大量实验证明，与各种在线持续学习算法相结合，DropTop 的平均准确率可以提高高达 10.4％，遗忘率减少高达 63.2％。

在线持续学习的自适应快速修正偏差

Adaptive Shortcut Debiasing for Online Continual Learning

Deep-learning models can extract a rich assortment of features from data.
Which features a model uses depends not only on predictivity-how reliably a
feature indicates train-set labels-but also on availability-how easily the
feature can be extracted, or leveraged, from inputs. The literature on shortcut
learning has noted examples in which models privilege one feature over another,
for example texture over shape and image backgrounds over foreground objects.
Here, we test hypotheses about which input properties are more available to a
model, and systematically study how predictivity and availability interact to
shape models' feature use. We construct a minimal, explicit generative
framework for synthesizing classification datasets with two latent features
that vary in predictivity and in factors we hypothesize to relate to
availability, and quantify a model's shortcut bias-its over-reliance on the
shortcut (more available, less predictive) feature at the expense of the core
(less available, more predictive) feature. We find that linear models are
relatively unbiased, but introducing a single hidden layer with ReLU or Tanh
units yields a bias. Our empirical findings are consistent with a theoretical
account based on Neural Tangent Kernels. Finally, we study how models used in
practice trade off predictivity and availability in naturalistic datasets,
discovering availability manipulations which increase models' degree of
shortcut bias. Taken together, these findings suggest that the propensity to
learn shortcut features is a fundamental characteristic of deep nonlinear
architectures warranting systematic study given its role in shaping how models
solve tasks.

深度学习模型从数据中提取多种多样的特征，但这些特征的使用不仅取决于其预测能力（即特征如何可靠地指示训练数据标签），还取决于其可提取性（即特征从输入中容易提取或利用的程度）。研究表明，某些模型更喜欢使用某些特定的特征，例如纹理优先于形状，图像背景优先于前景对象。本研究通过构建一个具有预测性和可提取性差异的最小显性生成框架，并量化了模型的 Shortcut Bias（即过度依赖快捷特征而忽视核心特征的程度），从而系统研究了预测性和可提取性如何相互作用来影响模型的特征使用。实证结果表明，线性模型相对没有偏见，但引入具有 ReLU 或 Tanh 单隐藏层的模型则会产生偏见。最后，本研究还研究了在实际使用中，模型是如何在自然数据集中权衡预测性和可提取性，并发现了增加模型 Shortcut Bias 程度的可提取性操作。综上所述，这些发现表明，在深度非线性架构中学习快捷特征的倾向是一个基本特征，值得进行系统研究，因为它影响模型解决任务的方式。

关于捷径学习的基础

On the Foundations of Shortcut Learning

Language bias is a critical issue in Visual Question Answering (VQA), where
models often exploit dataset biases for the final decision without considering
the image information. As a result, they suffer from performance drop on
out-of-distribution data and inadequate visual explanation. Based on
experimental analysis for existing robust VQA methods, we stress the language
bias in VQA that comes from two aspects, i.e., distribution bias and shortcut
bias. We further propose a new de-bias framework, Greedy Gradient Ensemble
(GGE), which combines multiple biased models for unbiased base model learning.
With the greedy strategy, GGE forces the biased models to over-fit the biased
data distribution in priority, thus makes the base model pay more attention to
examples that are hard to solve by biased models. The experiments demonstrate
that our method makes better use of visual information and achieves
state-of-the-art performance on diagnosing dataset VQA-CP without using extra
annotations.

本篇研究针对视觉问答 (VQA) 中的语言偏差问题进行了实验分析，提出了一种新的去偏差框架 GGE，该框架将多个有偏差的模型组合起来进行无偏差的基础模型学习，实现了更好地利用视觉信息，并在不使用额外注释的情况下，在 VQA-CP 数据集上达到了最先进的性能。