Counterfactual Generative NetworksDownload PDF

Published: 11 Apr 2022, Last Modified: 25 Jun 2024RC2021Readers: Everyone
Abstract: In this report, we attempt to verify the claims that the paper makes about their proposed CGN framework that decomposes the image generation process into independent causal mechanisms. Further, the author claims that these counterfactual images improves the out-of-distribution robustness of the classifier. We use the code provided by the authors to replicate several experiments in the original paper and draw conclusions based on these results.
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Paper Venue: ICLR 2021
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