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Papers/Invariant Risk Minimization

Invariant Risk Minimization

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz

2019-07-05Image ClassificationDomain GeneralizationOut-of-Distribution Generalization
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Abstract

We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions. To achieve this goal, IRM learns a data representation such that the optimal classifier, on top of that data representation, matches for all training distributions. Through theory and experiments, we show how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.

Results

TaskDatasetMetricValueModel
Image ClassificationColored-MNIST(with spurious correlation)Accuracy 66.9MLP-IRM

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