Image background features can constitute background bias (spurious
correlations) and impact deep classifiers decisions, causing shortcut learning
(Clever Hans effect) and reducing the generalization skill on real-world data.
The concept of optimizing Layer-wise Relevance Propagation (LRP) heatmaps, to
improve classifier behavior, was recently introduced by a neural network
architecture named ISNet. It minimizes background relevance in LRP maps, to
mitigate the influence of image background features on deep classifiers
decisions, hindering shortcut learning and improving generalization. For each
training image, the original ISNet produces one heatmap per possible class in
the classification task, hence, its training time scales linearly with the
number of classes. Here, we introduce reformulated architectures that allow the
training time to become independent from this number, rendering the
optimization process much faster. We challenged the enhanced models utilizing
the MNIST dataset with synthetic background bias, and COVID-19 detection in
chest X-rays, an application that is prone to shortcut learning due to
background bias. The trained models minimized background attention and hindered
shortcut learning, while retaining high accuracy. Considering external
(out-of-distribution) test datasets, they consistently proved more accurate
than multiple state-of-the-art deep neural network architectures, including a
dedicated image semantic segmenter followed by a classifier. The architectures
presented here represent a potentially massive improvement in training speed
over the original ISNet, thus introducing LRP optimization into a gamut of
applications that could not be feasibly handled by the original model.

利用 ISNet 架构及 Layer-wise Relevance Propagation (LRP) 热图优化，我们提出重新构建的架构，通过最小化背景相关性，防止深度分类器决策中的背景特征影响，从而减少捷径学习并提高泛化能力。在 MNIST 数据集和 COVID-19 胸部 X 射线检测中，我们的模型在最小化背景影响和阻止捷径学习的同时，保持了高准确性，相较于其他深度神经网络架构更具准确性，这表明这些架构在训练速度上是对 ISNet 的巨大改进。

深度神经网络背景偏差减轻的更快 ISNet

Faster ISNet for Background Bias Mitigation on Deep Neural Networks

Convolutional neural networks (CNNs) have achieved superhuman performance in
multiple vision tasks, especially image classification. However, unlike humans,
CNNs leverage spurious features, such as background information to make
decisions. This tendency creates different problems in terms of robustness or
weak generalization performance. Through our work, we introduce a contrastive
learning-based approach (CLAD) to mitigate the background bias in CNNs. CLAD
encourages semantic focus on object foregrounds and penalizes learning features
from irrelavant backgrounds. Our method also introduces an efficient way of
sampling negative samples. We achieve state-of-the-art results on the
Background Challenge dataset, outperforming the previous benchmark with a
margin of 4.1\%. Our paper shows how CLAD serves as a proof of concept for
debiasing of spurious features, such as background and texture (in
supplementary material).

本篇论文提出了一种基于对比学习的方法，称为 CLAD，用于减轻卷积神经网络中的背景偏差问题，并鼓励对物体前景进行语义关注，惩罚学习与背景无关的特征。通过在 Background Challenge 数据集上的实验，证明该方法超越了以往水平，并为抑制与背景和质地等有关的虚假特征提供了可行性证明。

CLAD：一种基于对比学习的背景去偏见方法

CLAD: A Contrastive Learning based Approach for Background Debiasing

In light of the success of contrastive learning in the image domain, current
self-supervised video representation learning methods usually employ
contrastive loss to facilitate video representation learning. When naively
pulling two augmented views of a video closer, the model however tends to learn
the common static background as a shortcut but fails to capture the motion
information, a phenomenon dubbed as background bias. Such bias makes the model
suffer from weak generalization ability, leading to worse performance on
downstream tasks such as action recognition. To alleviate such bias, we propose
\textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately
compose the moving foreground region of the selected video onto the static
background of others. Specifically, without any off-the-shelf detector, we
extract the moving foreground out of background regions via the frame
difference and color statistics, and shuffle the background regions among the
videos. By leveraging the semantic consistency between the original clips and
the fused ones, the model focuses more on the motion patterns and is debiased
from the background shortcut. Extensive experiments demonstrate that FAME can
effectively resist background cheating and thus achieve the state-of-the-art
performance on downstream tasks across UCF101, HMDB51, and Diving48 datasets.
The code and configurations are released at this https URL

为解决视频自监督学习中的 “背景偏差问题”，我们提出了前景 - 背景合并（FAME）方法，通过将选择的视频的移动前景区域合成到其他视频的静态背景中，去除背景的 shortcut，让模型更专注于运动模式，从而在动作识别等任务上取得了最先进的结果。