Two out of Three (ToT): using self-consistency to make robust predictions
This addresses the risk of deploying deep learning in critical applications where errors could have severe consequences, though it appears incremental as it builds on existing uncertainty estimation methods.
The paper tackles the problem of deep learning models making incomprehensible decisions in high-stakes domains by developing the Two out of Three (ToT) algorithm, which allows models to abstain from answering when uncertain by creating two alternative predictions to assess confidence.
Deep learning (DL) can automatically construct intelligent agents, deep neural networks (alternatively, DL models), that can outperform humans in certain tasks. However, the operating principles of DL remain poorly understood, making its decisions incomprehensible. As a result, it poses a great risk to deploy DL in high-stakes domains in which mistakes or errors may lead to critical consequences. Here, we aim to develop an algorithm that can help DL models make more robust decisions by allowing them to abstain from answering when they are uncertain. Our algorithm, named `Two out of Three (ToT)', is inspired by the sensitivity of the human brain to conflicting information. ToT creates two alternative predictions in addition to the original model prediction and uses the alternative predictions to decide whether it should provide an answer or not.