Evaluating Bregman Divergences for Probability Learning from Crowd
This work addresses the need for better probability learning in crowdsourcing applications, but it appears incremental as it focuses on adapting existing frameworks.
The paper tackled the problem of learning probability distributions from crowd data, rather than just discriminative models, by adapting Bregman divergences as objective functions in neural networks. The results indicate that careful design of the objective function and optimization in Keras is necessary, though no concrete numbers are provided.
The crowdsourcing scenarios are a good example of having a probability distribution over some categories showing what the people in a global perspective thinks. Learn a predictive model of this probability distribution can be of much more valuable that learn only a discriminative model that gives the most likely category of the data. Here we present differents models that adapts having probability distribution as target to train a machine learning model. We focus on the Bregman divergences framework to used as objective function to minimize. The results show that special care must be taken when build a objective function and consider a equal optimization on neural network in Keras framework.