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We introduce Resilient Multiple Choice Learning (rMCL), an extension of theMCL approach for conditional distribution estimation in regression settingswhere multiple targets may be sampled for each training input. Multiple ChoiceLearning is a simple framework to tackle multimodal density estimation, usingthe Winner-Takes-All (WTA) loss for a set of hypotheses. In regressionsettings, the existing MCL variants focus on merging the hypotheses, therebyeventually sacrificing the diversity of the predictions. In contrast, ourmethod relies on a novel learned scoring scheme underpinned by a mathematicalframework based on Voronoi tessellations of the output space, from which we canderive a probabilistic interpretation. After empirically validating rMCL withexperiments on synthetic data, we further assess its merits on the sound sourcelocalization problem, demonstrating its practical usefulness and the relevanceof its interpretation.

Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis