Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo
This work addresses uncertainty estimation for computer vision tasks, but it is incremental as it adapts existing methods to handle discrete parameters from Dropout.
The paper tackles the problem of predictive uncertainty estimation in classification by combining Dropout regularization with Hamiltonian Monte Carlo, achieving empirical success in improving generalization for difficult test examples.
Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging technique that tends to improve generalization in large scale models such as deep neural networks. Although, HMC provides convergence guarantees for most standard Bayesian models, it does not handle discrete parameters arising from Dropout regularization. In this paper, we present a robust methodology for improving predictive uncertainty in classification problems, based on Dropout and Hamiltonian Monte Carlo. Even though Dropout induces a non-smooth energy function with no such convergence guarantees, the resulting discretization of the Hamiltonian proves empirical success. The proposed method allows to effectively estimate the predictive accuracy and to provide better generalization for difficult test examples.