用扩散模型生成偏见数据，反向消除模型偏见。
Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
- 用条件扩散模型生成带有偏见的合成图像以强化偏见学习。
- 在多个基准数据集上超越现有最先进方法，提升模型泛化能力。
- 适合需要无监督去偏的深度学习研究者与工程师使用。
深度学习分类模型的性能常因训练数据中存在强伪相关性而受阻，这种属性与标签间的虚假关联导致模型产生偏见，进而造成预测时难以恢复的弱泛化。本文提出 Diffusing DeBias（DDB），一种新型无监督去偏方法，利用扩散模型在生成数据时固有的偏见学习倾向，通过条件扩散模型生成与偏见对齐的合成图像，替换原始训练集，用于训练有效的偏见放大模型。该模型随后被整合至端到端和两阶段无监督去偏框架中。本方法有效解决辅助模型学习中偏见冲突样本记忆的根本问题，在多个基准数据集上优于当前最先进方法，展现出作为通用高效去偏工具的潜力。代码已开源。
原文摘要 · Abstract (English)
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlations between specific attributes and target labels. This results in a form of bias affecting training data, which typically leads to unrecoverable weak generalization in prediction. This paper aims at facing this problem by leveraging bias amplification with generated synthetic data: we introduce Diffusing DeBias (DDB), a novel approach acting as a plug-in for common methods of unsupervised model debiasing exploiting the inherent bias-learning tendency of diffusion models in data generation. Specifically, our approach adopts conditional diffusion models to generate synthetic bias-aligned images, which replace the original training set for learning an effective bias amplifier model that we subsequently incorporate into an end-to-end and a two-step unsupervised debiasing approach. By tackling the fundamental issue of bias-conflicting training samples memorization in learning auxiliary models, typical of this type of techniques, our proposed method beats current state-of-the-art in multiple benchmark datasets, demonstrating its potential as a versatile and effective tool for tackling bias in deep learning models. Code is available at https://github.com/Malga-Vision/DiffusingDeBias
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。