In the image classification task, deep neural networks frequently rely on
bias attributes that are spuriously correlated with a target class in the
presence of dataset bias, resulting in degraded performance when applied to
data without bias attributes. The task of debiasing aims to compel classifiers
to learn intrinsic attributes that inherently define a target class rather than
focusing on bias attributes. While recent approaches mainly focus on
emphasizing the learning of data samples without bias attributes (i.e.,
bias-conflicting samples) compared to samples with bias attributes (i.e.,
bias-aligned samples), they fall short of directly guiding models where to
focus for learning intrinsic features. To address this limitation, this paper
proposes a method that provides the model with explicit spatial guidance that
indicates the region of intrinsic features. We first identify the intrinsic
features by investigating the class-discerning common features between a
bias-aligned (BA) sample and a bias-conflicting (BC) sample (i.e.,
bias-contrastive pair). Next, we enhance the intrinsic features in the BA
sample that are relatively under-exploited for prediction compared to the BC
sample. To construct the bias-contrastive pair without using bias information,
we introduce a bias-negative score that distinguishes BC samples from BA
samples employing a biased model. The experiments demonstrate that our method
achieves state-of-the-art performance on synthetic and real-world datasets with
various levels of bias severity.

通过为模型提供明确的空间引导，指示内在特征区域，本文提出了一种方法来增强相对未被充分利用的偏向特征，从而使模型在学习内在特征时得到指导。实验证明，我们的方法在具有不同程度偏见的合成和实际数据集上取得了最先进的性能。

通过研究偏差对比对的类别识别共同属性来增强内在特征进行去偏

Enhancing Intrinsic Features for Debiasing via Investigating  Class-Discerning Common Attributes in Bias-Contrastive Pair

The deep neural network (DNN) has been proven effective in various domains.
However, they often struggle to perform well on certain minority groups during
inference, despite showing strong performance on the majority of data groups.
This is because over-parameterized models learned \textit{bias attributes} from
a large number of \textit{bias-aligned} training samples. These bias attributes
are strongly spuriously correlated with the target variable, causing the models
to be biased towards spurious correlations (i.e., \textit{bias-conflicting}).
To tackle this issue, we propose a novel \textbf{re}weighted \textbf{s}parse
\textbf{t}raining framework, dubbed as \textit{\textbf{REST}}, which aims to
enhance the performance of biased data while improving computation and memory
efficiency. Our proposed REST framework has been experimentally validated on
three datasets, demonstrating its effectiveness in exploring unbiased
subnetworks. We found that REST reduces the reliance on spuriously correlated
features, leading to better performance across a wider range of data groups
with fewer training and inference resources. We highlight that the
\textit{REST} framework represents a promising approach for improving the
performance of DNNs on biased data, while simultaneously improving computation
and memory efficiency. By reducing the reliance on spurious correlations, REST
has the potential to enhance the robustness of DNNs and improve their
generalization capabilities. Code is released at
https://github.com/zhao1402072392/REST

通过重新权重稀疏训练的框架（REST），减少对偏见相关特征的依赖，改善深度神经网络（DNN）在有偏数据中的性能，同时提高计算和内存效率。