Conformalized Multimodal Uncertainty Regression and Reasoning
- URL: http://arxiv.org/abs/2309.11018v1
- Date: Wed, 20 Sep 2023 02:40:59 GMT
- Title: Conformalized Multimodal Uncertainty Regression and Reasoning
- Authors: Domenico Parente, Nastaran Darabi, Alex C. Stutts, Theja Tulabandhula,
and Amit Ranjan Trivedi
- Abstract summary: This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds.
We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries can result in multimodal uncertainties.
- Score: 0.9205582989348333
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces a lightweight uncertainty estimator capable of
predicting multimodal (disjoint) uncertainty bounds by integrating conformal
prediction with a deep-learning regressor. We specifically discuss its
application for visual odometry (VO), where environmental features such as
flying domain symmetries and sensor measurements under ambiguities and
occlusion can result in multimodal uncertainties. Our simulation results show
that uncertainty estimates in our framework adapt sample-wise against
challenging operating conditions such as pronounced noise, limited training
data, and limited parametric size of the prediction model. We also develop a
reasoning framework that leverages these robust uncertainty estimates and
incorporates optical flow-based reasoning to improve prediction prediction
accuracy. Thus, by appropriately accounting for predictive uncertainties of
data-driven learning and closing their estimation loop via rule-based
reasoning, our methodology consistently surpasses conventional deep learning
approaches on all these challenging scenarios--pronounced noise, limited
training data, and limited model size-reducing the prediction error by 2-3x.
Related papers
- Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity.
The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
arXiv Detail & Related papers (2023-08-03T12:43:21Z) - Quantifying Deep Learning Model Uncertainty in Conformal Prediction [1.4685355149711297]
Conformal Prediction is a promising framework for representing the model uncertainty.
In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations.
arXiv Detail & Related papers (2023-06-01T16:37:50Z) - Lightweight, Uncertainty-Aware Conformalized Visual Odometry [2.429910016019183]
Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics.
Emerging edge robotics devices like insect-scale drones and surgical robots lack a computationally efficient framework to estimate VO's predictive uncertainties.
This paper presents a novel, lightweight, and statistically robust framework that leverages conformal inference (CI) to extract VO's uncertainty bands.
arXiv Detail & Related papers (2023-03-03T20:37:55Z) - Uncertainty Quantification for Traffic Forecasting: A Unified Approach [21.556559649467328]
Uncertainty is an essential consideration for time series forecasting tasks.
In this work, we focus on quantifying the uncertainty of traffic forecasting.
We develop Deep S-Temporal Uncertainty Quantification (STUQ), which can estimate both aleatoric and relational uncertainty.
arXiv Detail & Related papers (2022-08-11T15:21:53Z) - Uncertainty estimation of pedestrian future trajectory using Bayesian
approximation [137.00426219455116]
Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy.
The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture.
The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
arXiv Detail & Related papers (2022-05-04T04:23:38Z) - Dense Uncertainty Estimation [62.23555922631451]
In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation.
We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
arXiv Detail & Related papers (2021-10-13T01:23:48Z) - DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner.
We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
arXiv Detail & Related papers (2021-02-16T23:50:35Z) - The Aleatoric Uncertainty Estimation Using a Separate Formulation with
Virtual Residuals [51.71066839337174]
Existing methods can quantify the error in the target estimation, but they tend to underestimate it.
We propose a new separable formulation for the estimation of a signal and of its uncertainty, avoiding the effect of overfitting.
We demonstrate that the proposed method outperforms a state-of-the-art technique for signal and uncertainty estimation.
arXiv Detail & Related papers (2020-11-03T12:11:27Z) - Learning to Predict Error for MRI Reconstruction [67.76632988696943]
We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error.
We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
arXiv Detail & Related papers (2020-02-13T15:55:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.