---
# System prepended metadata

title: Uncertainty

---

# Uncertainty
- OpenMix: Exploring Outlier Samples for Misclassification Detection (**CVPR** 2023) [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_OpenMix_Exploring_Outlier_Samples_for_Misclassification_Detection_CVPR_2023_paper.pdf) [[code]](https://github.com/Impression2805/OpenMix)
- Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective (**ICRA** 2023) [[paper]](https://arxiv.org/ftp/arxiv/papers/2301/2301.04421.pdf)
- A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification (**ICLR** 2023) [[paper]](https://openreview.net/pdf?id=YnkGMIh0gvX) [[code]](https://github.com/IML-DKFZ/fd-shifts)
- What can we learn from the selective prediction and uncertainty estimation performance of 523 imagenet classifiers (**ICLR** 2023) [[paper]](https://arxiv.org/pdf/2302.11874.pdf) [[code]](https://github.com/IdoGalil/benchmarking-uncertainty-estimation-performance)
- Towards Better Selective Classification  (**ICLR** 2023) [[paper]](https://openreview.net/pdf?id=5gDz_yTcst) [[code]](https://github.com/BorealisAI/towards-better-sel-cls)
- AUC-based Selective Classification (**AISTATS** 2023) [[paper]](https://proceedings.mlr.press/v206/pugnana23a/pugnana23a.pdf)
- Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed (**TMLR** 2022) [[paper]](https://openreview.net/pdf?id=VBHuLfnOMf) [[code]](https://github.com/melanibe/failure_detection_benchmark)
- Rethinking Confidence Calibration for Failure Prediction (**ECCV** 2022) [[paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136850512.pdf) [[code]](https://github.com/Impression2805/FMFP)
- Improving the Reliability for Confidence Estimation (**ECCV** 2022) [[paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136870385.pdf)
- Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness (**ECCV** 2022) [[paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730362.pdf)
- Single model uncertainty estimation via stochastic data centering(**NeurIPS** 2022) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2022/file/392d0d05e2f514063e6ce6f8b370834c-Paper-Conference.pdf)
- RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness(**NeurIPS** 2021) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2022/file/5ddcfaad1cb72ce6f1a365e8f1ecf791-Paper-Conference.pdf)[[code]](https://github.com/FrancescoPinto/RegMixup)
- Learning to predict trustworthiness with steep slope loss (**NeurIPS** 2021) [[paper]](https://arxiv.org/pdf/2110.00054.pdf) [[code]](https://github.com/luoyan407/predict_trustworthiness)
- Confidence-Aware Learning for Deep Neural Networks (**ICML** 2020) [[paper]](http://proceedings.mlr.press/v119/moon20a/moon20a.pdf) [[code]](https://github.com/daintlab/confidence-aware-learning)
- Self-Adaptive Training: beyond Empirical Risk Minimization (**NeurIPS** 2020) [[paper]](https://arxiv.org/pdf/2002.10319.pdf) [[code]](https://github.com/LayneH/self-adaptive-training)
- Selectivenet: A deep neural network with an integrated reject option (**ICML** 2019) [[paper]](http://proceedings.mlr.press/v97/geifman19a/geifman19a.pdf)[[code]](https://github.com/geifmany/selectivenet)
- Addressing Failure Prediction by Learning Model Confidence (**NeurIPS** 2019) [[paper]](https://proceedings.neurips.cc/paper/2019/file/757f843a169cc678064d9530d12a1881-Paper.pdf) [[code]](https://github.com/valeoai/ConfidNet)
- Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (**NeurIPS** 2019) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf)
- Deep Gamblers: Learning to Abstain with Portfolio Theory (**NeurIPS** 2019) [[paper]](https://arxiv.org/pdf/1907.00208.pdf) [[code]](https://github.com/Z-T-WANG/NIPS2019DeepGamblers)
- Using self-supervised learning can improve model robustness and uncertainty(**NeurIPS** 2019) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2019/file/a2b15837edac15df90721968986f7f8e-Paper.pdf)[[code]](https://github.com/hendrycks/ss-ood)
- To Trust Or Not To Trust A Classifier (**NeurIPS** 2018) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2018/file/7180cffd6a8e829dacfc2a31b3f72ece-Paper.pdf) [[code]](https://github.com/google/TrustScore)
- Selective classification for deep neural networks (**NeurIPS** 2017) [[paper]](https://proceedings.neurips.cc/paper/2017/file/4a8423d5e91fda00bb7e46540e2b0cf1-Paper.pdf)[[code]](https://github.com/geifmany/selective_deep_learning)
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks (**ICLR** 2017) [[paper]](https://arxiv.org/pdf/1610.02136.pdf)
- What uncertainties do we need in bayesian deep learning for computer vision? (**NeurIPS** 2017) [[paper]](https://proceedings.neurips.cc/paper_files/paper/2017/file/2650d6089a6d640c5e85b2b88265dc2b-Paper.pdf)
- On calibration of modern neural networks （ICML 2017） [[paper]](https://arxiv.org/pdf/1706.04599.pdf)
- Simple and scalable predictive uncertainty estimation using deep ensembles(**NeurIPS** 2017)
[[paper]](https://arxiv.org/pdf/1612.01474.pdf)
- Dropout as a bayesian approximation: Representing model uncertainty in deep learning(**ICML** 2016)[[paper]](https://proceedings.mlr.press/v48/gal16.pdf)