Counterfactual contrastive learning enhances downstream task performance by incorporating domain-specific information through realistic image synthesis. The proposed CF-SimCLR method outperforms standard SimCLR by explicitly aligning domains in learned representations. Evaluation on chest radiography and mammography datasets shows significant improvements in robustness to acquisition shift, especially for under-represented domains. The lightweight counterfactual inference model used requires minimal computational overhead compared to the contrastive learning process.
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by Melanie Rosc... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.09605.pdfDeeper Inquiries