Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Abstract: Trying to capture the sample-label relationship, conditional generative models often end up inheriting the spurious correlation in the training dataset, giving label-conditional distributions that are severely imbalanced in another latent attribute. To mitigate such undesirable correlations engraved into generative models, which we call spurious causality, we propose a general two-step strategy. (a) Fairness Intervention (FI): Emphasize the minority samples that are hard to be generated due to the spurious correlation in the training dataset. (b) Corrective Sampling (CS): Filter the generated samples explicitly to follow the desired label-conditional latent attribute distribution. We design the fairness intervention for various degrees of supervision on the spurious attribute, including unsupervised, weakly-supervised, and semi-supervised scenarios. Our experimental results show that the proposed FICS can successfully resolve the spurious correlation in generated samples on various datasets.
Submission Length: Regular submission (no more than 12 pages of main content)
Video: https://youtu.be/RDhLKJnZTjE
Supplementary Material: zip
Assigned Action Editor: ~Aditya_Menon1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 932
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