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About this paper
Changjie Lu
The survey maps causal representation learning for vision and proposes directions for medical imaging. It describes learning latent causal features from images and testing predictions under interventions or counterfactuals; identifying causes and a causal model is difficult. In synthetic Colored MNIST, an IRM-trained MLP reaches 66.9% test accuracy versus 17.1% for ERM; this supports the constructed shift, not clinical generalization.
Summary by alphaXiv.
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