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Problems such as predicting a new shading field (Y) for an image (X) areambiguous: many very distinct solutions are good. Representing this ambiguityrequires building a conditional model P(Y|X) of the prediction, conditioned onthe image. Such a model is difficult to train, because we do not usually havetraining data containing many different shadings for the same image. As aresult, we need different training examples to share data to produce goodmodels. This presents a danger we call "code space collapse" - the trainingprocedure produces a model that has a very good loss score, but whichrepresents the conditional distribution poorly. We demonstrate an improvedmethod for building conditional models by exploiting a metric constraint ontraining data that prevents code space collapse. We demonstrate our model ontwo example tasks using real data: image saturation adjustment, imagerelighting. We describe quantitative metrics to evaluate ambiguous generationresults. Our results quantitatively and qualitatively outperform differentstrong baselines.

CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional Variational Generation