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Abstract : We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.
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Submitted on : Monday, January 14, 2013 - 1:39:59 PM
Last modification on : Thursday, February 3, 2022 - 3:01:40 AM
Long-term archiving on: : Monday, April 15, 2013 - 4:00:53 AM


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  • HAL Id : hal-00773605, version 1



M. Pawan Kumar, Ben Packer, Daphne Koller. MODELING LATENT VARIABLE UNCERTAINTY FOR LOSS-BASED LEARNING. ICML, Jun 2012, Edinburgh, United Kingdom. ⟨hal-00773605⟩



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