# Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

arXiv: https://arxiv.org/abs/2206.07551 · HTML: https://mlsanity.com/paper/2206.07551/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 60 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [VisDA: The Visual Domain Adaptation Challenge](https://mlsanity.com/paper/1710.06924/) (2017)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Universal Domain Adaptation through Self Supervision](https://mlsanity.com/paper/2002.07953/) (2020)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [Open Set Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1804.10427/) (2018)
- [Understanding Self-Training for Gradual Domain Adaptation](https://mlsanity.com/paper/2002.11361/) (2020)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Cycle Self-Training for Domain Adaptation](https://mlsanity.com/paper/2103.03571/) (2021)
- [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020)
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://mlsanity.com/paper/2003.08823/) (2020)
- [SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2012.11460/) (2020)

## Built on by (most cited first, 21 of 21)

- [Open-Set Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2405.19899/) (2024)
- [Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance](https://mlsanity.com/paper/2308.05986/) (2023)
- [Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data](https://mlsanity.com/paper/2311.01420/) (2023)
- [DEVIAS: Learning Disentangled Video Representations of Action and Scene](https://mlsanity.com/paper/2312.00826/) (2023)
- [Unknown Domain Inconsistency Minimization for Domain Generalization](https://mlsanity.com/paper/2403.07329/) (2024)
- [Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation](https://mlsanity.com/paper/2310.05453/) (2023)
- [Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework](https://mlsanity.com/paper/2411.12558/) (2024)
- [Unknown Sample Discovery for Source Free Open Set Domain Adaptation](https://mlsanity.com/paper/2312.03767/) (2023)
- [COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation](https://mlsanity.com/paper/2409.00397/) (2024)
- [Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation](https://mlsanity.com/paper/2506.03521/) (2025)
- [COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation](https://mlsanity.com/paper/2308.10450/) (2023)
- [Self-Paced Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2303.05933/) (2023)
- [Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach](https://mlsanity.com/paper/2311.00285/) (2023)
- [ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction](https://mlsanity.com/paper/2507.21516/) (2025)
- [Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming](https://mlsanity.com/paper/2510.18363/) (2025)
- [Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation](https://mlsanity.com/paper/2603.15475/) (2026)
- [Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation](https://mlsanity.com/paper/2404.10574/) (2024)
- [Open-Set Domain Generalization through Spectral-Spatial Uncertainty Disentanglement for Hyperspectral Image Classification](https://mlsanity.com/paper/2506.09460/) (2025)
- [In defense of the two-stage framework for open-set domain adaptive semantic segmentation](https://mlsanity.com/paper/2601.01439/) (2026)
- [SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification](https://mlsanity.com/paper/2605.03371/) (2026)
- [Locality-aware Private Class Identification for Domain Adaptation with Extreme Label Shift](https://mlsanity.com/paper/2605.05567/) (2026)

## Abstract (arXiv)

Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain. Existing domain adversarial learning methods are not suitable for OSDA because distribution matching with textitunknown classes leads to negative transfer. Previous OSDA methods have focused on matching the source and the target distribution by only utilizing textitknown classes. However, this textitknown-only matching may fail to learn the target-textitunknown feature space. Therefore, we propose Unknown-Aware Domain Adversarial Learning (UADAL), which textitaligns the source and the target-textitknown distribution while simultaneously textitsegregating the target-textitunknown distribution in the feature alignment procedure. We provide theoretical analyses on the optimized state of the proposed textitunknown-aware feature alignment, so we can guarantee both textitalignment and textitsegregation theoretically. Empirically, we evaluate UADAL on the benchmark datasets, which shows that UADAL outperforms other methods with better feature alignments by reporting state-of-the-art performances.