mlsanityData to 14 Oct 2026

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning

arXiv 2205.14917 ↗First posted 30 May 2022cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural networks is already resource demanding and so is also their uncertainty quantification. In this overview article, we survey methods that we developed to teach DNNs to be uncertain when they encounter new object classes. Additionally, we present training methods to learn from only a few labels with help of uncertainty quantification. Note that this is typically paid with a massive overhead in computation of an order of magnitude and more compared to ordinary network training. Finally, we survey our work on neural architecture search which is also an order of magnitude more resource demanding then ordinary network training.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

33 of 46 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  7. 2018YOLOv3: An Incremental Improvementcited by 2,659
  8. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  9. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  10. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  11. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  12. 2017Deep Bayesian Active Learning with Image Datacited by 819
21 more
  1. 2016Playing for Data: Ground Truth from Computer Gamescited by 818
  2. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  3. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  4. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  5. 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
  6. 2017Domain Adaptation for Visual Applications: A Comprehensive Surveycited by 128
  7. 2017MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networkscited by 94
  8. 2018Sparse DNNs with Improved Adversarial Robustnesscited by 72
  9. 2019Towards neural networks that provably know when they don't knowcited by 69
  10. 2018Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilitiescited by 54
  11. 2018CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentationcited by 49
  12. 2020MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Mapscited by 23
  13. 2019Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networkscited by 20
  14. 2019Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Imagescited by 18
  15. 2020MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detectioncited by 18
  16. 2021Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectorscited by 16
  17. 2019Detection of False Positive and False Negative Samples in Semantic Segmentationcited by 9
  18. 2019MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentationcited by 8
  19. 2019Reliable Multi-label Classification: Prediction with Partial Abstentioncited by 6
  20. 2019The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentationcited by 4
  21. 2022Detecting and Learning the Unknown in Semantic Segmentationcited by 4

Built on by

Not cited yet.