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Learning Independent Causal Mechanisms
Nov 03, 2017 (modified: Nov 03, 2017)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Independent causal mechanisms are a central concept in the study of causal-
ity with implications for machine learning tasks. In this work we develop
an algorithm to recover a set of (inverse) independent mechanisms relating
a distribution transformed by the mechanisms to a reference distribution.
The approach is fully unsupervised and based on a set of experts that com-
pete for data to specialize and extract the mechanisms. We test and analyze
the proposed method on a series of experiments based on image transfor-
mations. Each expert successfully maps a subset of the transformed data
to the original domain, and the learned mechanisms generalize to other
domains. We discuss implications for domain transfer and links to recent
trends in generative modeling.
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