# SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation arXiv: https://arxiv.org/abs/2203.15202 ยท HTML: https://mlsanity.com/paper/2203.15202/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 50 references that are in the map. - First posted on arXiv 29 Mar 2022. ## Builds on (most cited first, 30 of 34) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016) - [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020) - [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016) - [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016) - [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018) - [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020) - [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019) - [Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2101.10979/) (2021) - [Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift](https://mlsanity.com/paper/2006.10963/) (2020) - [Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2003.03773/) (2020) - [Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision](https://mlsanity.com/paper/2004.07703/) (2020) - [2017 Robotic Instrument Segmentation Challenge](https://mlsanity.com/paper/1902.06426/) (2019) - [Source-Free Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2103.16372/) (2021) - [Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/2003.00867/) (2020) - [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020) - [Iterative Learning with Open-set Noisy Labels](https://mlsanity.com/paper/1804.00092/) (2018) - [Provably End-to-end Label-Noise Learning without Anchor Points](https://mlsanity.com/paper/2102.02400/) (2021) - [A Second-Order Approach to Learning with Instance-Dependent Label Noise](https://mlsanity.com/paper/2012.11854/) (2020) - [Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.11249/) (2021) - [Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization](https://mlsanity.com/paper/2102.02414/) (2021) - [Learning with Bounded Instance- and Label-dependent Label Noise](https://mlsanity.com/paper/1709.03768/) (2017) - [DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2107.09600/) (2021) - [MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2103.05254/) (2021) - [Source-Free Open Compound Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2106.03422/) (2021) - [Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation](https://mlsanity.com/paper/1907.02882/) (2019) - [Affinity Space Adaptation for Semantic Segmentation Across Domains](https://mlsanity.com/paper/2009.12559/) (2020) - [Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering](https://mlsanity.com/paper/1608.04290/) (2016) - [EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels](https://mlsanity.com/paper/2011.05704/) (2020) ## Built on by (most cited first, 17 of 17) - [Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation](https://mlsanity.com/paper/2403.12505/) (2024) - [360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes](https://mlsanity.com/paper/2404.16501/) (2024) - [Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation](https://mlsanity.com/paper/2303.05194/) (2023) - [Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler](https://mlsanity.com/paper/2409.17555/) (2024) - [Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2206.07994/) (2022) - [Condition-Invariant Semantic Segmentation](https://mlsanity.com/paper/2305.17349/) (2023) - [FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions](https://mlsanity.com/paper/2407.13437/) (2024) - [Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization](https://mlsanity.com/paper/2412.18342/) (2024) - [SegDA: Maximum Separable Segment Mask with Pseudo Labels for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2308.05851/) (2023) - [OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning](https://mlsanity.com/paper/2407.02386/) (2024) - [Masked autoencoders are effective solution to transformer data-hungry](https://mlsanity.com/paper/2212.05677/) (2022) - [Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation](https://mlsanity.com/paper/2309.10649/) (2023) - [EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels](https://mlsanity.com/paper/2510.12687/) (2025) - [Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation](https://mlsanity.com/paper/2307.00893/) (2023) - [Clean Label Disentangling for Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2311.16580/) (2023) - [Structured prototype regularization for synthetic-to-real driving scene parsing](https://mlsanity.com/paper/2603.16083/) (2026) - [Unsupervised Collaborative Domain Adaptation for Driving Scene Parsing](https://mlsanity.com/paper/2606.01818/) (2026) ## Abstract (arXiv) This paper studies a practical domain adaptive (DA) semantic segmentation problem where only pseudo-labeled target data is accessible through a black-box model. Due to the domain gap and label shift between two domains, pseudo-labeled target data contains mixed closed-set and open-set label noises. In this paper, we propose a simplex noise transition matrix (SimT) to model the mixed noise distributions in DA semantic segmentation and formulate the problem as estimation of SimT. By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i.e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT. Specifically, volume regularization minimizes the volume of simplex formed by rows of the non-square SimT, which ensures outputs of segmentation model to fit into the ground truth label distribution. To compensate for the lack of open-set knowledge, anchor guidance and convex guarantee are devised to facilitate the modeling of open-set noise distribution and enhance the discriminative feature learning among closed-set and open-set classes. The estimated SimT is further utilized to correct noise issues in pseudo labels and promote the generalization ability of segmentation model on target domain data. Extensive experimental results demonstrate that the proposed SimT can be flexibly plugged into existing DA methods to boost the performance. The source code is available at https://github.com/CityU-AIM-Group/SimT.