Deep neural networks (DNNs) have exhibited remarkable performance across
various tasks, yet their susceptibility to spurious correlations poses a
significant challenge for out-of-distribution (OOD) generalization. Spurious
correlations refer to erroneous associations in data that do not reflect true
underlying relationships but are instead artifacts of dataset characteristics
or biases. These correlations can lead DNNs to learn patterns that are not
robust across diverse datasets or real-world scenarios, hampering their ability
to generalize beyond training data. In this paper, we propose an
autoencoder-based approach to analyze the nature of spurious correlations that
exist in the Global Wheat Head Detection (GWHD) 2021 dataset. We then use
inpainting followed by Weighted Boxes Fusion (WBF) to achieve a 2% increase in
the Average Domain Accuracy (ADA) over the YOLOv5 baseline and consistently
show that our approach has the ability to suppress some of the spurious
correlations in the GWHD 2021 dataset. The key advantage of our approach is
that it is more suitable in scenarios where there is limited scope to adapt or
fine-tune the trained model in unseen test environments.

通过自动编码器方法分析了 Global Wheat Head Detection（GWHD）2021 数据集中存在的错误关联，并使用修复和加权盒融合方法成功提高了平均域准确率（ADA），证明了该方法能够抑制部分 GWHD 2021 数据集中的错误关联。

基于自编码器的消除伪相关性方法

Autoencoder based approach for the mitigation of spurious correlations

Association Rule Mining (ARM) is the task of learning associations among data
features in the form of logical rules. Mining association rules from
high-dimensional numerical data, for example, time series data from a large
number of sensors in a smart environment, is a computationally intensive task.
In this study, we propose an Autoencoder-based approach to learn and extract
association rules from time series data (AE SemRL). Moreover, we argue that in
the presence of semantic information related to time series data sources,
semantics can facilitate learning generalizable and explainable association
rules. Despite enriching time series data with additional semantic features, AE
SemRL makes learning association rules from high-dimensional data feasible. Our
experiments show that semantic association rules can be extracted from a latent
representation created by an Autoencoder and this method has in the order of
hundreds of times faster execution time than state-of-the-art ARM approaches in
many scenarios. We believe that this study advances a new way of extracting
associations from representations and has the potential to inspire more
research in this field.

基于自动编码器的方法可以从时间序列数据中学习和提取关联规则，同时利用语义信息可以促进学习可推广和可解释的关联规则。实验证明，从自动编码器创建的潜在表示中可以提取语义关联规则，并且在许多情况下，该方法的执行时间比现有技术的关联规则挖掘方法快几百倍。这项研究在关联性的提取方法上有推动作用，并有潜力激发更多相关领域的研究。