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Preprint
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
arXiv, version 1
05/16/2020
Abstract
In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework. The proposed framework takes as input "teacher" and student model architectures and a general posterior expectation of interest. The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples. We focus on the posterior predictive distribution and expected entropy as distillation targets. We investigate several aspects of this framework including the impact of uncertainty and the choice of student model architecture. We study methods for student model architecture search from a speed-storage-accuracy perspective and evaluate down-stream tasks leveraging entropy distillation including uncertainty ranking and out-of-distribution detection.
Files and links (2)
Details
- Title
- Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks
- Edition
- version 1
- Resource Type
- Preprint
- Publisher
- arXiv
- Format
- pdf and link
- Number of pages
- 23
- Copyright
- © the authors
- Identifiers
- 99381512043606600
- Academic Unit
- Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English