# Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling

## TL;DR

This paper proposes a novel latent distribution assumption for aggregating noisy labels, allowing for multiple subjective labels per object, which improves modeling in ambiguous labeling scenarios.

## Contribution

It introduces a new latent distribution assumption that accounts for subjective label variability, enhancing consensus modeling for ambiguous data.

## Key findings

- Better performance on difficult, ambiguous tasks
- More suitable for scenarios with label uncertainty
- Improves unbiased and consistent consensus estimation

## Abstract

We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model parameters using the observed noisy labels. A traditional assumption underlying previously introduced generative models is that each object has one latent true label. In contrast, we introduce a novel latent distribution assumption, implying that a unique true label for an object might not exist, but rather each object might have a specific distribution generating a latent subjective label each time the object is observed. Our experiments showed that the novel assumption is more suitable for difficult tasks, when there is an ambiguity in choosing a "true" label for certain objects.