  We introduce Resilient Multiple Choice Learning (rMCL), an extension of the
MCL approach for conditional distribution estimation in regression settings
where multiple targets may be sampled for each training input. Multiple Choice
Learning is a simple framework to tackle multimodal density estimation, using
the Winner-Takes-All (WTA) loss for a set of hypotheses. In regression
settings, the existing MCL variants focus on merging the hypotheses, thereby
eventually sacrificing the diversity of the predictions. In contrast, our
method relies on a novel learned scoring scheme underpinned by a mathematical
framework based on Voronoi tessellations of the output space, from which we can
derive a probabilistic interpretation. After empirically validating rMCL with
experiments on synthetic data, we further assess its merits on the sound source
localization problem, demonstrating its practical usefulness and the relevance
of its interpretation.
