Deep neural networks (DNNs), despite their impressive ability to generalize over-capacity networks, often rely heavily on malignant bias as shortcuts instead of task-related information for discriminative tasks. To address this problem, recent studies utilize auxiliary information related to the bias, which is rarely obtainable in practice, or sift through a handful of bias-free samples for debiasing. However, the success of these methods is not always guaranteed due to the unfulfilled presumptions. In this paper, we propose a novel method, Contrastive Debiasing via Generative Bias-transformation (CDvG), which works without explicit bias labels or bias-free samples. Motivated by our observation that not only discriminative models but also image translation models tend to focus on the malignant bias, CDvG employs an image translation model to transform one bias mode into another while preserving the task-relevant information. Additionally, the bias-transformed views are set against each other through contrastive learning to learn bias-invariant representations. Our method demonstrates superior performance compared to prior approaches, especially when bias-free samples are scarce or absent. Furthermore, CDvG can be integrated with the methods that focus on bias-free samples in a plug-and-play manner for additional enhancements, as demonstrated by diverse experimental results.

翻译：深度神经网络（DNN）尽管具有强大的过容量泛化能力，但在判别任务中往往过度依赖恶性偏置作为捷径，而非任务相关信息。针对该问题，近年研究利用与偏置相关的辅助信息（实践中难以获取），或筛选少量无偏样本进行去偏。然而，这些方法因假设条件难以满足而无法保证成功。本文提出一种新方法——基于生成式偏置转换的对比去偏（CDvG），该方法无需显式偏置标签或无偏样本。受"不仅判别模型，图像翻译模型同样倾向于聚焦恶性偏置"这一观察启发，CDvG采用图像翻译模型在保留任务相关信息的同时，将一种偏置模式转换为另一种。此外，通过对比学习使偏置转换后的视图相互对抗，以学习偏置不变表征。实验表明，我们的方法在无偏样本稀缺或缺失时性能显著优于现有方法。同时，CDvG能够以即插即用方式与聚焦无偏样本的方法集成以增强效果，多样化实验结果充分证实了这一点。