Twincher learns bijective representations of observations aligned with continuous system parameters to enable robust iterative inversion, showing better data efficiency and noise tolerance than standard inverse modeling on synthetic systems.
Learning ABCs: Approximate bijective correspondence for isolating factors of variation with weak supervision
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Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems
Twincher learns bijective representations of observations aligned with continuous system parameters to enable robust iterative inversion, showing better data efficiency and noise tolerance than standard inverse modeling on synthetic systems.