Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks

The deployment of large-scale deep neural networks in safety-critical scenariosrequires quantifiably calibrated and reliable measures of trust. Unfortunately,existing algorithms to achieve risk-awareness are complex and adhoc. We presentcapsa, an open-source and flexible framework for unifying these methods andcreating risk-aware models. We unify state-of-the-art risk algorithms under thecapsa framework, propose a composability method for combining different riskestimators together in a single function set, and benchmark on high-dimensionalperception tasks. Code is available at: https://github.com/themis-ai/capsa