mlsanityData to 14 Oct 2026

Latent Discriminant deterministic Uncertainty

arXiv 2207.10130 ↗First posted 20 Jul 2022cs.CV cs.LGcited by 17

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Abstract (arXiv)

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in the context of autonomous driving perception tasks. Recently proposed Deterministic Uncertainty Methods (DUM) can only partially meet such requirements as their scalability to complex computer vision tasks is not obvious. In this work we advance a scalable and effective DUM for high-resolution semantic segmentation, that relaxes the Lipschitz constraint typically hindering practicality of such architectures. We learn a discriminant latent space by leveraging a distinction maximization layer over an arbitrarily-sized set of trainable prototypes. Our approach achieves competitive results over Deep Ensembles, the state-of-the-art for uncertainty prediction, on image classification, segmentation and monocular depth estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 62 of its 86 references that are in the map. First posted on arXiv 20 Jul 2022.

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62 of 86 references.

References · 62

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2017On Calibration of Modern Neural Networkscited by 4,960
  5. 2017Neural Discrete Representation Learningcited by 4,712
  6. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  7. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  8. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  9. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  10. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  11. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  12. 2016Pyramid Scene Parsing Networkcited by 2,479
38 more
  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2017Improved Training of Wasserstein GANscited by 2,235
  5. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  6. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  7. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  8. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  9. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  10. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 968
  11. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  12. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  13. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  14. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  15. 2019Deep Ensembles: A Loss Landscape Perspectivecited by 443
  16. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  17. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  18. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  19. 2020BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learningcited by 371
  20. 2018Invertible Residual Networkscited by 365
  21. 2020Calibrating Deep Neural Networks using Focal Losscited by 357
  22. 2017Sparsity Invariant CNNscited by 326
  23. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  24. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  25. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  26. 2019Addressing Failure Prediction by Learning Model Confidencecited by 186
  27. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  28. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  29. 2020Efficient and Scalable Bayesian Neural Nets with Rank-1 Factorscited by 171
  30. 2019Ensemble Distribution Distillationcited by 158
  31. 2018Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flowcited by 135
  32. 2018Robust Classification with Convolutional Prototype Learningcited by 123
  33. 2020Learning Open Set Network with Discriminative Reciprocal Pointscited by 115
  34. 2019Benchmarking the Robustness of Semantic Segmentation Modelscited by 111
  35. 2018LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Imagescited by 88
  36. 2019How do neural networks see depth in single images?cited by 82
  37. 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
  38. 2021Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentationcited by 40

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 4; 2025: 2; 2026: 2.

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  1. 2023Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Modelscited by 16
  2. 2024Towards Understanding and Quantifying Uncertainty for Text-to-Image Generationcited by 11
  3. 2023Uncertainty-Aware Decision Transformer for Stochastic Driving Environmentscited by 8
  4. 2024SURE: SUrvey REcipes for building reliable and robust deep networkscited by 8
  5. 2023Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regressioncited by 7
  6. 2023Improving CLIP Robustness with Knowledge Distillation and Self-Trainingcited by 7
  7. 2023Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximizationcited by 7
  8. 2023Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidancecited by 6
  9. 2025Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perceptioncited by 6
  10. 2023The Robust Semantic Segmentation UNCV2023 Challenge Resultscited by 5
  11. 2023Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learningcited by 3
  12. 2024Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methodscited by 2
5 more
  1. 2025A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimationcited by 2
  2. 2023Measuring and Modeling Uncertainty Degree for Monocular Depth Estimationcited by 1
  3. 2024Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Trainingcited by 1
  4. 2026CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Modelcited by 0
  5. 2026CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Drivingcited by 0