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Beauty Learning and Counterfactual Inference
[article]
2019
arXiv
pre-print
This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been discussed metaphysically for centuries and been proved exists, is quantifiable, and can be learned by deep models in our recent paper, where we utilize a natural image generator
arXiv:1904.12629v1
fatcat:lz3oavltxjdefal5tep7bix6nm