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.

翻译：深度神经网络（DNNs）在各种任务中展现出卓越性能，但其对伪相关性的敏感性对分布外（OOD）泛化构成了重大挑战。伪相关性指的是数据中存在的错误关联，这些关联并不反映真实的潜在关系，而是数据集特性或偏差的产物。这些相关性可能导致DNNs学习到在不同数据集或现实场景中并不鲁棒的模式，从而削弱其在训练数据之外的泛化能力。本文提出一种基于自编码器的方法，用于分析Global Wheat Head Detection（GWHD）2021数据集中存在的伪相关性本质。我们随后采用修复技术结合加权框融合（WBF），在YOLOv5基线模型上将平均域精度（ADA）提升了2%，并一致表明我们的方法能够抑制GWHD 2021数据集中的部分伪相关性。我们方法的关键优势在于，在训练模型对未见测试环境的适应或微调空间有限的场景中更为适用。