Resolving label uncertainty with implicit generative modelsDownload PDF

Anonymous

Sep 29, 2021 (edited Nov 21, 2021)ICLR 2022 Conference Blind SubmissionReaders: Everyone
  • Keywords: weakly supervised learning, generative models, image segmentation
  • Abstract: In prediction problems, coarse and imprecise sources of input can provide rich information about labels, but are not readily used by discriminative learners. In this work, we propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs. This formulation unifies various machine learning settings: the weak beliefs can come in the form of noisy or incomplete labels, likelihoods given by a different prediction mechanism on auxiliary input, or common-sense priors reflecting knowledge about the structure of the problem at hand. We demonstrate the proposed algorithms on diverse problems: classification with negative training examples, learning from rankings, weakly and self-supervised aerial imagery segmentation, co-segmentation of video frames, and coarsely supervised text classification.
  • One-sentence Summary: Unified approach to learning with uncertain targets, applied to a variety of machine learning settings.
  • Supplementary Material: zip
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