mlsanityData to 14 Oct 2026

Robustness via Uncertainty-aware Cycle Consistency

arXiv 2110.12467 ↗First posted 24 Oct 2021cs.CV cs.LG eess.IVcited by 16

Not on the map. Why?

Abstract (arXiv)

Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty, leading to performance degradation when encountering unseen perturbations at test time. To address this, we propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC), which models the per-pixel residual by generalized Gaussian distribution, capable of modelling heavy-tailed distributions. We compare our model with a wide variety of state-of-the-art methods on various challenging tasks including unpaired image translation of natural images, using standard datasets, spanning autonomous driving, maps, facades, and also in medical imaging domain consisting of MRI. Experimental results demonstrate that our method exhibits stronger robustness towards unseen perturbations in test data. Code is released here: https://github.com/ExplainableML/UncertaintyAwareCycleConsistency.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 37 of its 69 references that are in the map. First posted on arXiv 24 Oct 2021.

Builds on

37 of 69 references.

References · 37

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25 more
  1. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  2. 2020Deep Unfolding Network for Image Super-Resolutioncited by 254
  3. 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
  4. 2018MedGAN: Medical Image Translation using GANscited by 152
  5. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  6. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
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  9. 2019Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentationcited by 75
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  17. 2019Adversarial Self-Defense for Cycle-Consistent GANscited by 21
  18. 2021Memory-guided Unsupervised Image-to-image Translationcited by 21
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 2; 2025: 2; 2026: 1.

16 papers.

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  1. 2022BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networkscited by 23
  2. 2023Calibrating Multimodal Learningcited by 17
  3. 2023ProbVLM: Probabilistic Adapter for Frozen Vision-Language Modelscited by 7
  4. 2023USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolutioncited by 4
  5. 2025Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarioscited by 4
  6. 2023Likelihood Annealing: Fast Calibrated Uncertainty for Regressioncited by 3
  7. 2022Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networkscited by 2
  8. 2022What can we learn about a generated image corrupting its latent representation?cited by 1
  9. 2023Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstructioncited by 1
  10. 2023Uncertainty Aware Neural Network from Similarity and Sensitivitycited by 1
  11. 2023HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiogramscited by 1
  12. 2024Uncertainty-Aware Regularization for Image-to-Image Translationcited by 1
4 more
  1. 2023Uncertainty-Driven Multi-Scale Feature Fusion Network for Real-time Image Derainingcited by 0
  2. 2024Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learningcited by 0
  3. 2025Uncertainty-aware Test-Time Training (UT3) for Efficient On-the-fly Domain Adaptive Dense Regressioncited by 0
  4. 2026Towards Reliable AI-Based Histological Staining: A Systematic Study of Scaling and Uncertainty in Unpaired Generative Modelscited by 0