Deep models trained on large amounts of data often incorporate implicit
biases present during training time. If later such a bias is discovered during
inference or deployment, it is often necessary to acquire new data and retrain
the model. This behavior is especially problematic in critical areas such as
autonomous driving or medical decision-making. In these scenarios, new data is
often expensive and hard to come by. In this work, we present a method based on
change penalization that takes a pre-trained model and adapts the weights to
mitigate a previously detected bias. We achieve this by tuning a
zero-initialized copy of a frozen pre-trained network. Our method needs very
few, in extreme cases only a single, examples that contradict the bias to
increase performance. Additionally, we propose an early stopping criterion to
modify baselines and reduce overfitting. We evaluate our approach on a
well-known bias in skin lesion classification and three other datasets from the
domain shift literature. We find that our approach works especially well with
very few images. Simple fine-tuning combined with our early stopping also leads
to performance benefits for a larger number of tuning samples.

基于改变惩罚的方法来减轻模型中的隐性偏见，需要很少的对立示例并结合提前停止准则以增加性能。

通过调整并对变化进行惩罚来减少预训练模型中的偏差

Reducing Bias in Pre-trained Models by Tuning while Penalizing Change

In this paper, we studied two identically-trained neural networks (i.e.
networks with the same architecture, trained on the same dataset using the same
algorithm, but with different initialization) and found that their outputs
discrepancy on the training dataset exhibits a "double descent" phenomenon. We
demonstrated through extensive experiments across various tasks, datasets, and
network architectures that this phenomenon is prevalent. Leveraging this
phenomenon, we proposed a new early stopping criterion and developed a new
method for data quality assessment. Our results show that a phenomenon-driven
approach can benefit deep learning research both in theoretical understanding
and practical applications.

本文研究了两个具有相同架构、使用相同算法和数据集训练的神经网络，发现在训练数据上，它们的输出差异表现出 “双峰现象”。通过广泛实验跨越各种任务、数据集和网络架构，我们证明了这种现象的普遍性。利用这种现象，我们提出了一种新的早期停止准则并开发了一种新的数据质量评估方法。我们的结果表明，基于现象的方法可以在理论理解和实际应用方面有益于深度学习研究。