Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. None of the previous methods are able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform the first systematic exploration into training-free uncertainty estimation. 
 We propose three simple and scalable methods to analyze the variance of output from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by other state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to other training-required state-of-the-art methods. Last but not least, we demonstrate that the uncertainty from our proposed methods can be used to improve the neural network training.

A set of methods to obtain uncertainty estimation of any given model without re-designing, re-training, or to fine-tuning it. Describes several approaches for measuring uncertainty in arbitrary neural networks when there is an absence of distortion during training.