通过聚类感知网络消除语义偏差，提升模型在分布外数据上的鲁棒性。
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
- 基于孪生网络重构语义空间，抑制被用作捷径的潜在聚类
- 在4个基准上平均提升5.3~11.9%的F1分数，轻量模型收益更显著
- 无需数据增强或重写，适用于视觉与自然语言任务
捷径学习损害模型对分布外数据的泛化能力。我们发现，样本嵌入语义分布的不平衡会引发虚假语义相关性，破坏模型鲁棒性。为此，提出SCISSOR（语义聚类干预以抑制捷径），一种基于孪生网络的去偏方法，通过抑制被用作捷径的潜在隐空间聚类来重构语义空间。与以往数据去偏方法不同，SCISSOR无需数据增强或重写。在6个模型、4个基准上评估：计算机视觉任务中Chest-XRay和Not-MNIST，自然语言处理任务中GYAFC和Yelp。相比多个基线，SCISSOR在GYAFC上提升+5.3，Yelp上+7.3，Chest-XRay上+7.7，Not-MNIST上+1。对轻量模型尤为有利：视觉任务中ViT提升约9.5%，自然语言任务中BERT提升约11.9%。本研究重新定义了模型泛化的范式，确立了SCISSOR作为缓解捷径学习、构建更鲁棒、抗偏见AI系统的基础框架。
原文摘要 · Abstract (English)
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances in the semantic distribution of sample embeddings induce spurious semantic correlations, compromising model robustness. To address this issue, we propose SCISSOR (Semantic Cluster Intervention for Suppressing ShORtcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Unlike prior data-debiasing approaches, SCISSOR eliminates the need for data augmentation and rewriting. We evaluate SCISSOR on 6 models across 4 benchmarks: Chest-XRay and Not-MNIST in computer vision, and GYAFC and Yelp in NLP tasks. Compared to several baselines, SCISSOR reports +5.3 absolute points in F1 score on GYAFC, +7.3 on Yelp, +7.7 on Chest-XRay, and +1 on Not-MNIST. SCISSOR is also highly advantageous for lightweight models with ~9.5% improvement on F1 for ViT on computer vision datasets and ~11.9% for BERT on NLP. Our study redefines the landscape of model generalization by addressing overlooked semantic biases, establishing SCISSOR as a foundational framework for mitigating shortcut learning and fostering more robust, bias-resistant AI systems.
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